Hardware stocks are the future, and will only to continue to grow with AI. Grow your portfolio forward with these three stocks

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Its no secret that the hardware industry stands to gain a lot from the boom in artificial intelligence. After all, it is the continued growth of the tech hardware industry that makes developments, such asAI, more available to the public.

The global computer hardware market is expected to grow to $914.55 billion by 2028 at a 6.5% CAGR. While this growth comes from a myriad of factors, one of the more notable ones include the expansion of data centers.

All of the cloud serviceindustry leaders, Amazon (NASDAQ:AMZN), Google (NASDAQ:GOOG, NASDAQ:GOOGL) and Microsoft (NASDAQ:MSFT), have announced expansions of data centers for the storage and processing needs in hosting AI applications. Here are three hardware stocks for AI that I have identified as excellent picks.

Seagate (STX)

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Seagate (NASDAQ:STX) is a data storage company that broadly covers the data storage industry. The companys focuses include cloud storage systems and services, hard drives and solid state drives. Yahoo! Finance has 27 analysts predicting a 1-year price range on STX to be between $55.00 and $119.00, with a mean of $99.60.

STX did not show strong revenue, but profitability for the company has greatly improved. While revenue suffered a year-on-year loss, net profit margin has increased 106.49% YOY. Evidently, the companys improvement in profitability is characterized in its 941.13% YOY growth in free cash flow. STX even shows signs of being undervalued with a -11.45 price to book ratio, well below the sector median.

STX is a great buy beyond the financials. Seagate has partnered itself with Ebay to expand its Hard Drive Singularity Program. By selling refurbished and recertified hard drives on Ebay, this program improves Seagate revenue while also promoting sustainability. Seagate has also launched a new hard drive platform: the Mozaic 3+. The 30 terabyte Mosaic 3+ Exos hard drive has the same carbon footprint as a standard 16 terabyte hard drive, maximizing efficiency and furthering sustainability efforts. STX is one of the hardware stocks for AI that investors have to add to their portfolios.

Arista Networks (ANET)

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Arista Networks (NYSE:ANET) is a computer networking company that develops and manufactures components for large data centers and high-performance computing. Yahoo! Finance reports 25 analysts predicting a 1-year price range on ANET between $220.00 and $389.00, with a mean of $323.77.

ANET has shown very strong financials for Q1 2024. The companys profitability has shown great improvement, as earnings per share is up 39.16% YOY. Revenue has shown excellent growth, as seen through a 46.1% YOY net income increase. Management has done an excellent job at increasing revenue while maintaining profitability, with revenue growth being more than double operating expenses growth.

Arista has partnered with NVIDIA (NASDAQ:NVDA) to develop an AI data center with lowered job completion times. Specifically, this network will streamline the use of generative AI applications for clients, saving clients time and growing ANETs revenue by being the first choice in AI data centers. Arista has also introduced Universal Network Observability (UNO) on its operational tasks platform CloudVision. UNO improves troubleshooting abilities for clients through AI-driven analysis and recommendations for network issues. ANET is one of the hardware stocks for AI for me because of its advancements and new services that emphasize improving client experiences.

Pure Storage (PSTG)

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Pure Storage (NYSE:PSTG) is a data storage platform that aims to simplify IT and offer data security to its clients. The company has approximately 60% of the Fortune 500 as clients. Yahoo! Finance reports 19 analysts predicting a 1-year price range on ANET between $44.00 and $80.00, with a mean of $70.51.

PSTG boasts strong financials for Q1 2024. The company beat revenue and earnings expectations for Q1 2024, having demonstrated impressive jumps in both revenue and profitability. Revenue grew 17.68% YOY and EPS grew a whopping 300% YOY. This profitability growth is even more impressive when seeing the balance sheet, as management has made sure that asset growth has more than doubledliability growth YOY.

Pure Storage has made an investment in Landing AI, a visual AI solutions company innovating in the space of Large Visual Models. Additionally, Landing AI develops AI tools that analyze visual data. This aligns closely with PSTGs goal as a data storage platform, and the companies will share common interests. Pure Storage has enhanced its cybersecurity offerings with improved ransomware recover and AI-based anomaly detection. These innovations specifically boost data security. For instance, the anomaly detection analyzing historical data of clients anomaly patterns to identify threats faster. PSTG is one of the hardware stocks for AI for investors.

On the date of publication, Matthew Rodriguesdid not hold (either directly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Matthew Rodrigues is a college student studying Business at UC Berkeley Haas. He believes detailed research and correct interpretation of current events is what leads to investment success.

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3 Hardware Stocks to Buy for the Artificial Intelligence Era - InvestorPlace

The new maintenance coordinator at an apartment complex in Dallas has been getting kudos from tenants and colleagues for good work and late-night assistance. Previously, the eight people on the propertys staff, managing the buildings 814 apartments and town homes, were overworked and putting in more hours than they wanted.

Besides working overtime, the new staff member at the complex, the District at Cypress Waters, is available 24/7 to schedule repair requests and doesnt take any time off.

Thats because the maintenance coordinator is an artificial intelligence bot that the property manager, Jason Busboom, began using last year. The bot, which sends text messages using the name Matt, takes requests and manages appointments.

The team also has Lisa, the leasing bot that answers questions from prospective tenants, and Hunter, the bot that reminds people to pay rent. Mr. Busboom chose the personalities he wanted for each A.I. assistant: Lisa is professional and informative; Matt is friendly and helpful; and Hunter is stern, needing to sound authoritative when reminding tenants to pay rent.

The technology has freed up valuable time for Mr. Busbooms human staff, he said, and everyone is now much happier in his or her job. Before, when someone took vacation, it was very stressful, he added.

Chatbots as well as other A.I. tools that can track the use of common areas and monitor energy use, aid construction management and perform other tasks are becoming more commonplace in property management. The money and time saved by the new technologies could generate $110 billion or more in value for the real estate industry, according to a report released in 2023 by McKinsey Global Institute. But A.I.s advances and its catapult into public consciousness have also stirred up questions about whether tenants should be informed when theyre interacting with an A.I. bot.

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Landlords Have Started Using A.I. Chatbots to Manage Properties - The New York Times

When ChatGPT took everyone by storm in November 2022, it was OpenAI, the startup behind it, that seized the business worlds attention. But, as usual, big tech is back on the front foot. Nvidia, maker of accelerator chips that are at the core of generative artificial intelligence (AI), is duelling with Microsoft, a tech giant of longer standing, to be the worlds most valuable company. Like Microsoft, it is investing in a diverse ecosystem of startups that it hopes will strengthen its lead. Predictably, given the techlash mindset of the regulatory authorities, both firms are high on the watch list of antitrust agencies.

Dont roll your eyes. The trustbusters may have infamously overreached in recent years in their attempts to cut big firms down to size. Yet for years big-tech incumbents in Silicon Valley and elsewhere have shown just as infamous a tendency to strut imperiously across their digital domains. What is intriguing is the speed at which the antitrust authorities are operating. Historically, such investigations have tended to be labyrinthine. It took 40 years for the Supreme Court to order E.I. Du Pont de Nemours, a large American chemical firm, to divest its anticompetitive stake in General Motors, which it first started to acquire in 1917 when GM was a fledgling carmaker. The Federal Trade Commission (FTC), an American antitrust agency, is still embroiled in a battle with Meta, a social-media giant, to unwind Facebooks acquisitions of Instagram and WhatsApp, done 12 and ten years ago, respectively.

Originally posted here:

Is artificial intelligence making big tech too big? - The Economist

AFTER SAM ALTMAN was sacked from OpenAI in November of 2023, a meme went viral among artificial-intelligence (AI) types on social media. What did Ilya see? it asked, referring to Ilya Sutskever, a co-founder of the startup who triggered the coup. Some believed a rumoured new breakthrough at the company that gave the world ChatGPT had spooked Mr Sutskever.

Although Mr Altman was back in charge within days, and Mr Sutskever said he regretted his move, whatever Ilya saw appears to have stuck in his craw. In May he left OpenAI. And on June 19th he launched Safe Superintelligence (SSI), a new startup dedicated to building a superhuman AI. The outfit, whose other co-founders are Daniel Gross, a venture capitalist, and Daniel Levy, a former OpenAI researcher, does not plan to offer any actual products. It has not divulged the names of its investors.

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A new lab and a new paper reignite an old AI debate - The Economist

They say, It all begins with a phone call, and they were right. A family member had fallen and broken her hip. We were suddenly immersed in her care. When we werent at work, we were at the facility, in a hospital waiting room, on the phone with service providers, or in a care meeting. There was very little sleep, lots of worry, sporadic panic and overall overwhelmingness.

I am an action person. In times of uncertainty, I busy myself with overcomplicated tasks to keep myself distracted. One day when I was walking out of the health care facility, I arbitrarily decided the walker that the hospital provided was dangerous too tippy so I found the Cadillac of walkers on Amazon. I was about to buy it with one click when I saw a woman with the same walker by the door and asked her if she liked it.

She said, I like it so much, I have two. She didnt need the brand-new second one and offered to sell it to me for half price!

I told her Id be back in 30 minutes. I just needed to run home and get cash.

When I got there, Fourteen said, Oh good, youre home. My sports physical is in 10 minutes. Wed enrolled him in high school two days earlier, and he needed the sports physical that day to start conditioning for football. With a couple of phone calls and some fancy driving on I-4, we made it all happen.

Realizing Im entering the sandwich generation portion of my journey isnt the only flashing neon midlife crisis sign Ive seen lately. Weeks before the phone call, Id made a very unexpected decision to pursue a move onto the admin team at school. Ive been a kindergarten teacher for eight years and taught preschool before that. I was extremely comfortable in my role. In fact, every time I had an HR meeting, I told them wild horses couldnt drag me from my classroom. Here was my wild horse: The preschool director was leaving to pursue new opportunities.

Tom Peters, author of In Search of Excellence, said, If a window of opportunity appears, dont pull down the shade.

Before I knew it, I found myself texting our schools director: Id like to be considered for the preschool director position. I dont know whats more mid-life-y an impulsive career change or reading quotes about opportunity?

Amid all this chaos, we found ourselves relying on the Wendys drive-thru for way too many meals. Then Wendys replaced their human order-takers with an AI kiosk. Last night, when the robot asked me what kind of sauce Id like with one of my Biggie Bags, I said, Barbecue.

It told me barbecue sauce wasnt an option. So I said, Honey mustard.

It said, Barbecue sauce is not an option.

I asked what the options were.

It said, Sweet and sour, honey mustard, and barbecue.

I said, No sauce.

It asked me what Id like to drink.

Sprite.

It replied, Barbecue sauce is not an option.

As I shook my fist and screamed for it to get off my lawn, a human being came over the speaker and took my order. I was frustrated by technology another sign of midlife crisis.

And last, but certainly not the least difficult to process, was trying to passively enjoy an episode of Dateline one evening (wait, theres more) when I heard arguably the best song of 2001, Missy Elliots Get Ur Freak On. Assuming Id accidentally switched to a music channel, I looked up in sheer horror to see it was an Applebees commercial.

That, my friends, is when I decided its time to (1) embrace that music will never be as good as it was 20 years ago, (2) give myself time and grace to figure out how to navigate high schools and assisted living communities simultaneously, and (3) trade in all my jeans that are getting too tight (thanks, hormones!) for comfy Amazon two-piece sets with elastic waists.

Welcome to midlife. I think Im going to like it here.

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When midlife hits hard: Signs from work, family, artificial intelligence and Applebees - The Community Paper

Should Artificial Intelligence Supply Plain Meaning? The 11th Circuit Wants to Know

Insurance coverage lawsuits often hinge on the plain and ordinary meaning of specific words or phrases. But not every word in an insurance policy can be defined. Yet without stable and predictable definitions, neither policyholders nor insurers can establish a clear and consistent scope of coverage. In a recent concurring opinion, Eleventh Circuit Judge Kevin Newsom suggests that artificial intelligence (AI) large language models (LLMs) could help resolve these definitional debates. His opinion inSnell v. United Specialty Insurance Company, No. 22-12581, 2024 WL 2717700 (11th Cir. May 28, 2024) highlights the pros and cons of calling upon technology to supply plain meaning.

This approach may even offer promise for a fundamental issue plaguing the insurability of AI risk, whichwe discussed last month. That is, how to define AI to ensure a functional and predictable scope of coverage?

LLMs as a Tool in the Interpretive Toolkit

InSnell, an insured sought coverage under a Commercial General Liability policy in connection with a lawsuit brought after a child sustained injuries while using an in-ground trampoline. The insurer denied coverage and refused to defend the lawsuit. The lawsuit alleged that Snell, a landscaper, negligently installed the trampoline in a clients backyard. The district court found that coverage would turn on whether installation of the trampoline amounted to landscaping, as that term was used in the policy. But the policy did not supply a definition for the term landscaping. The court, therefore, turned to the common, everyday meaning of the term, which the district court found to not include trampoline installation.

The Eleventh Circuit ultimately affirmed the district courts decision based on Alabama-law specific grounds unrelated to the meaning of landscaping. Yet, of particular note, in a concurring opinion, Judge Newsom suggested that LLMs like OpenAIs ChatGPT, Googles Gemini and Anthropics Claude could help discern the ordinary meaning of undefined words in legal instruments, including insurance policies.

Judge Newsom identified several benefits to using LLMs for this purpose. LLMs train on vast amounts of ordinary-language datamuch more than might be available through a dictionarymaking them particularly adept at determining common usage. They understand context, which helps detect nuanced language patterns. LLMs are also increasingly accessible, making their use an inexpensive research tool for judges, lawyers and ordinary citizens. In addition, using LLMs to facilitate ordinary meaning interpretation enhances the transparency of the interpreting process because queries and outputs from LLMs can be fully disclosed to the public.

Judge Newsom also pointed out potential drawbacks. LLMs can hallucinate and generate incorrect information at times. They might not fully capture offline speech, potentially missing how underrepresented groups use language. There is also the risk that lawyers, judges or litigants may try to manipulate LLMs to favor their preferred interpretation. Moreover, over-reliance on LLMs could lead to dystopian robo judges.

Despite these concerns, Judge Newsom suggests that AI could be another tool in the judiciarys toolbox, alongside dictionaries and semantic canons.

Can LLMs Define AI?

Asdiscussed previously, we have identified the general inability to clearly and uniformly define AI for purposes of insuring AI risks. Following Judge Newsoms suggestion, we asked ChatGPT to provide a stable and predictable definition of AI for use in insurance policies to answer the question posed in our last article. That is, how should insurance policies define AI to ensure a functional scope of coverage? Heres ChatGPTs response:

Artificial Intelligence (AI) refers to the capability of a machine to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and natural language understanding. AI systems include technologies like machine learning algorithms, neural networks, and natural language processing, which enable these machines to analyze data, recognize patterns, and make decisions or predictions based on that analysis.

ChatGPT even explained why its definition is a good one. It explained that this definition is comprehensive, covering all AI capabilities and ensuring all current and foreseeable technologies are included. It is clear and specific, identifying key components of AI systems, and technologically neutral, making it adaptable to future advancements. By focusing on AIs functional aspects, it aligns with practical insurance concerns. This definition provides a clear framework, reducing disputes and aligning with existing legal frameworks, making it easier to use in legal contexts. It also minimizes differing interpretations, leading to more straightforward claims processes and fewer litigation risks. Whether this AI-generated definition or some iteration might provide a functional definition for purposes of insurance remains to be seen.

Conclusion

As policyholders and insurers work to resolve the age-old task of supplying meaning to undefined terms, or defining new risks like those posed by AI, they might find it useful to follow Judge Newsoms recommendation and use AI among the other tools in their toolkits to resolve definitional debates. For now, however, while landscapers and acrobats can rest assured knowing that trampolines are not landscaping (at least in the 11thCircuit), the more vexing insurance-related AI issue remains: whatisAI?

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Should Artificial Intelligence Supply Plain Meaning? The 11th Circuit Wants to Know - Hunton Andrews Kurth LLP

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Artificial intelligence companies are riding a hyper-accelerated growth curve. Like the crack of a starting gun, the November 2022 launch of ChatGPT awakened the world to the vast potential of AIparticularly generative AI. As more companies invest in machine learning, automation, robotics, and AI-based data analytics solutions, the AI algorithm has quickly become the foundational technology of business.

This list of AI companies chronicles this growth by reflecting the dynamic shifts disrupting the tech industry. It covers the full ecosystem of AI vendors: new generative AI companies, entrenched giants, AI purveyors across verticals, and upstart visionaries. Theres no telling which will most influence AIs future, but we believe that the players on this list as a whole will profoundly reshape technology and, as a direct result, the arts, retail, and the entirety of culture.

Its no coincidence that this top AI companies list is composed mostly of cloud providers. Artificial intelligence requires massive storage and compute power at the level provided by the top cloud platforms. These cloud leaders are offering a growing menu of AI solutions to existing clients, giving them an enormous competitive advantage in the battle for AI market share. The cloud leaders represented also have deep pockets, which is key to their success, as AI development is exceptionally expensive.

Enterprise leader in AI

As a dominant provider of enterprise solutions and a cloud leaderits Azure Cloud is second only to AWSMicrosoft has invested heavily in AI, with plenty to show for it. For example, it has significantly expanded its relationship with OpenAI, the creator of ChatGPT, leading to the development of intelligent AI copilots and other generative AI technologies that are embedded or otherwise integrated with Microsofts products. Leveraging its massive supercomputing platform, its goal is to enable customers to build out AI applications on a global scale. With its existing infrastructure and partnerships, current trajectory, and penchant for innovation, its likely that Microsoft will be the leading provider of AI solutions to the enterprise in the long run.

Top-tier managed services for cloud and AI

As the top dog in the all-important world of cloud computing, few companies are better positioned than AWS to provide AI services and machine learning to a massive customer base. In true AWS fashion, its profusion of new tools is endless and intensely focused on making AI accessible to enterprise buyers. AWSs long list of AI services includes quality control, machine learning, chatbots, automated speech recognition, and online fraud detection. It is one of the best providers of innovative AI managed services.

To learn about new direction in generative AI, see the eWeek video: AWS VP Bratin Saha on the Bedrock Generative AI Tools

Leading generative AI for technical and non-technical audiences

As the most successful search giant of all time, Googles historic strength is in algorithms, which is the very foundation of AI. Though Google Cloud is perennially a distant third in the cloud market, its platform is a natural conduit to offer AI services to customers. The Gemini ecosystem has proven especially popular and innovative, combining access to generative AI infrastructure, developer tools, and a user-friendly natural language interface. The company is also heavily focused on responsible AI and communicating how it is working toward an ethical AI approach.

Founder of Watson and watsonx AI solutions

A top hybrid and multicloud vendor, boosted by its acquisition of Red Hat in 2019, IBMs deep-pocketed global customer base has the resources to invest heavily in AI. IBM has an extensive AI portfolio, highlighted by the Watson platform, with strengths in conversational AI, machine learning, and automation. The company invests deeply in R&D and has a treasure trove of patents; its AI alliance with MIT will also likely fuel unique advances in the future.

Leading provider of GPUs and other AI infrastructure

All roads lead to Nvidia as AIespecially generative AI and larger modelsgrows ever more important. At the center of Nvidias strength is the companys wicked-fast GPUs, which provide the power and speed for compute-intensive AI applications. Additionally, Nvidia offers a full suite of software solutions, from generative AI to AI training to AI cybersecurity. It also has a network of partnerships with large businesses to develop AI and frequently funds AI startups.

For an in-depth look a how generative AI and advanced hardware are changing security, see the eWeek video: Nvidia CSO David Reber on AI and Cybersecurity

Embedded AI assistance in social media apps

Metathe parent company of Facebook, Instagram, and many other popular platformshas had a slightly slower start on generative AI than some of the other tech giants, but it has nonetheless blazed through to create some of the most ubiquitous and innovative solutions on the market today. Metas Llama 3, for example, is one of the largest and easiest to access LLMs on the market today, as it is open source and available for research and commercial use. The company is also very transparent with its own AI research and resources. Most recently, Meta has developed Meta AI, an intelligent assistant that can operate in the background of Facebook, Messenger, Instagram, and WhatsApp.

Chinese innovator in AI and quantum computing

Little known in the U.S., Baidu owns the majority of the internet search market in China. The companys AI platform, Baidu Brain, processes text and images and builds user profiles. With the most recent generation, Baidu Brain 6.0, quantum computing capabilities have also expanded significantly. It has also launched its own ChatGPT-like tool, a generative AI chatbot called Ernie Bot.

Leader in cloud-based AI support

Oracles cloud platform has leapt forward over the past few yearsits now one of the top cloud vendorsand its cloud strength will be a major conduit for AI services to come. To bulk up its AI credentials, Oracle has partnered with Nvidia to boost enterprise AI adoption. The company stresses its machine learning and automation offerings and also sells a menu of prebuilt models to enable faster AI deployment.

To find out how a cloud leader is facing the challenges of todays IT sector, see the eWeek video: Oracle Clouds Leo Leung on Cloud Challenges and Solutions

Cloud leader and innovator in APAC region

Alibaba, a Chinese e-commerce giant and leader in Asian cloud computing, split into six divisions, each empowered to raise capital. Of particular note is the Alibaba Cloud Intelligence group, which handles cloud and AI innovations and products. While Alibaba has been greatly hampered by government crackdowns, observers see the Cloud Intelligence group as a major support of AI development. The company is also working to optimize a ChatGPT-like tool.

For more information about todays leading generative AI software, see our guide: Top 20 Generative AI Tools & Applications

Think of these AI companies as the forward-looking cohort that is inventing and supporting the systems that propel AI forward. Its a mixed bunch with diverse approaches to AI, some more directly focused on AI tools than others. Note that most of these pioneer companies were founded between 2009 and 2013, long before the ChatGPT hype cycle.

These companies are at the center of a debate about who will have the most control over the future of AI. Will it be these agile and innovative pioneers, or the giant cloud vendors that have the deep infrastructure that AI needs and can sell their AI tools to an already-captive customer base?

Founder of ChatGPT

The world was forever changed when OpenAI debuted ChatGPT in November 2022a major milestone in the history of artificial intelligence. Founded in 2015 with $1 billion in seed funding, San Francisco-based OpenAI benefits from a cloud partnership with Microsoft, which has invested a rumored $13 billion in OpenAI. Not content to rest on its success, OpenAI has launched GPT-4, a larger multimodal version of its successful LLM foundation model, and continues to innovate in areas like text-to-video generation. The company also offers DALL-E, which creates artistic images from user text prompts.

Industry-focused AI solutions and services

Founded in 2009, C3.ai is part of a new breed of vendors that can be called an AI vendor: not a legacy tech company that has shifted into AI but a company created specifically to sell AI solutions to the enterprise. The company offers a long menu of turnkey AI solutions so companies can deploy AI without the complexity of building it themselves. Clients include the U.S. Air Force, which uses AI to predict system failure, and Shell, which uses C3.ai to monitor equipment across its sprawling infrastructure.

For in-depth comparison of C3.ai and a major competitor, see our guide: C3.ai vs. DataRobot: Top Cloud AI Platforms

Solutions provider for generative and predictive AI

Founded in 2011, H2O.ai is another company built from the ground up with the mission of providing AI software to the enterprise. H2O focuses on democratizing AI. This means that while AI has traditionally been available only to a few, H2O works to make AI practical for companies without major in-house AI expertise. With solutions for AI middleware, AI in-app stores, and AI applications, the company claims thousands of customers for its H2O Cloud.

To learn how computers can see the world around them, watch our eWeek video: H2O.ais Prashant Natarajan on AI and Computer Vision

Cloud-agnostic AI and data solutions

Founded in 2012, DataRobot offers an AI Cloud thats cloud-agnostic, so it works with all the cloud leaders (AWS, Azure, and Google, for example). Its built with a multicloud architecture that offers a single platform accessible to all manner of data professionals. Its value is that it provides data pros with deep AI support to analyze data, which supercharges data analysis and processing. Among its outcomes is faster and more flexible machine learning model creation.

For in-depth comparison of DataRobot and a major competitor, read DataRobot vs. H2O.ai: Top Cloud AI Platforms.

Next-gen data warehouse and AI data cloud vendor

Founded in 2012, Snowflake is a next-gen data warehouse vendor. Artificial intelligence requires oceanic amounts of data, properly prepped, shaped, and processed, and supporting this level of data crunching is one of Snowflakes strengths. Operating across AWS, Microsoft Azure, and Google Cloud, Snowflakes AI Data Cloud aims to eliminate data silos for optimized data gathering and processing.

For an expert take on how todays IT platforms are enabling wider data access, see the eWeek video: Snowflakes Torsten Grabs on AI and Democratizing Data

Low-code/no-code AI/ML model development platform

Founded in 2013, Dataiku is a vendor with an AI and machine learning platform that aims to democratize tech by enabling both data professionals and business professionals to create data models. Using shareable dashboards and built-in algorithms, Dataiku users can spin up machine learning or deep learning models; most helpfully, it allows users to create models without writing code.

End-to-end data analytics and AI workflows

Since RapidMiner was acquired by Altair in 2022, the vendor has continued to grow and improve its no-code AI app-building features, which allow non-technical users to create applications without writing software. The company also offers a no-code MLOps solution that uses a containerized approach. As a sign of the times, users can build models using a visual, code-based, or automated approach, depending on their preference.

Unified AI orchestration solution provider

Founded in 2013, Domino Data Lab offers both comprehensive AIOps and MLOps (machine learning operations) solutions through its platform technology. With its enterprise AI platform, users can easily manage their data, software, apps, APIs, and other infrastructural elements in a unified ecosystem. Users have the option to work with hybrid or multicloud orchestration, and they can also choose between a SaaS or self-managed approach. Domino Data Lab has partnered with Nvidia to provide a faster development environment, so expect more innovation from them soon.

To learn how todays software developers are finding ways to work faster, see the eWeek video: Domino Data Labs Jack Parmer on Code First Data Science

AI-optimized data lakehouses and infrastructure

Founded in 2013, Databricks offers an enterprise data intelligence platform that supports the flexible data processing needed to create successful AI and ML deployments; think of this data solution as the crucial building block of artificial intelligence. Through its innovative data storage and management technology, Databricks ingests and preps data from myriad sources. Its data management and data governance tools work with all major cloud players. The company is best known for its integration of the data warehouse (where the data is processed) and the data lake (where the data is stored) into a data lakehouse format.

Interested in the relationship between AI and Data? See the eWeek video: Databrickss Chris DAgostino on AI and Data Management

AI solutions for graphic designers and creatives

Adobe is a SaaS company that primarily offers marketing and creative tools to its users. The company has begun to enhance all of these products with AI solutions, including Adobe Firefly, a robust generative AI tool and assistant that helps users personalize marketing assets, edit visual assets for better quality, and generally create creative content at scale across different Adobe suite products. In late 2023, Adobe expanded its AI capabilities through its acquisition of Rephrase.ai, a text-to-video studio solution.

Drag-and-drop approach to data and AI modeling

A prime example of a mega theme driving AI, Alteryxs goal is to make AI models easier to build. The goal is to abstract the complexity and coding involved with deploying artificial intelligence. The platform enables users to connect data sources to automated modeling tools through a drag-and-drop interface, allowing data professionals to create new models more efficiently. Users grab data from data warehouses, cloud applications, and spreadsheets, all in a visualized data environment. Alteryx was founded in 1997.

Learn about the major trend toward enabling wider access to data by watching the eWeek video: Alteryxs Suresh Vittal on the Democratization of Data Analytics

A conversational approach to generative content

Inflection AI labels itself as an AI studio that is looking to create advanced applied AI that can be used for more challenging use casesWhile it has hinted at other projects in the works, its primary product right now is Pi, a conversational AI that is designed to take a personalized approach to casual conversations. Pi can be accessed through pi.ai as well as iOS and Android apps. The company was founded by many former leaders from DeepMind, Google, OpenAI, Microsoft, and Meta, though several of these leaders have since left to work in the new Microsoft AI division of Microsoft. Its truly up in the air how this change will impact the company and Pi, though they expect to release an API in the near future.

Leading provider of AI for public sector use cases

Scale is an AI company that covers a lot of ground with its products and solutions, giving users the tools to build, scale, and customize AI modelsincluding generative AI modelsfor various use cases. The Scale Data Engine simplifies the process of collecting, preparing, and testing data before AI model development and deployment, while the Scale Generative AI Platform and Scale custom LLMs give users the ability to fine-tune generative AI to their specifications. Scale is also a leading provider of AI solutions for federal, defense, and public sector use cases in the government.

Leader in AI networking solutions

Arista Networks is a longstanding cloud computing and networking company that has quickly advanced its infrastructure and tooling to accommodate high-volume and high-frequency AI traffic. More specifically, the company has worked on its GPU and storage connections and sophisticated network operating software. Tools like the Arista Networks 7800 AI Spine and the Arista Extensible Operating System (EOS) are leading the way when it comes to giving users the self-service capabilities to manage AI traffic and network performance.

Hybrid, cloud-agnostic data platform

Having merged with former competitor Hortonworks, Cloudera now offers the Cloudera Data Platform and the Cloudera Machine Learning solution to help data pros collaborate in a unified platform that supports AI development. The ML solutions are specifically designed to perform data prep and predictive reporting. As an example of emerging trends, Cloudera provides portable cloud-native data analytics. Cloudera was founded in 2008.

For an inside view of where data leader Cloudera is headed, see the eWeek video: Clouderas Ram Venkatesh on the Cloudera Roadmap

Leader in blockchain, Web3, and metaverse technologies

Accubits is a blockchain, Web3, and metaverse tech solutions provider that has expanded its services and projects into artificial intelligence as well. The company primarily works to support other companies in their digital transformation efforts, offering everything from technology consulting to hands-on product and AI development. The companys main AI services include support for AI product and model development, consulting for generative AI projects, solution architecting, and automation solutions.

If the AI pioneers are a mixed bag, this group of AI visionaries is heading off in an even wider array of directions. These AI startups are closer to the edge, building a new vision even as they imagine ittheyre inventing the generative AI landscape in real time, in many cases. More than any technology before, theres no roadmap for the growth of AI, yet these generative AI startups are proceeding at full speed.

Generative AI leader committed to constitutional AI

Founded by two former senior members of OpenAI, Anthropics generative AI chatbot, Claude 3, provides detailed written answers to user questions; with this most recent generation, certain aspects of multimodality have been introduced while other components of the platform have been improved. In essence, its another tool that operates like ChatGPT, but with a twist: Anthropic publicly proclaims its focus on Constitutional AI, a methodology it has developed for consistent safety, transparency, and ethicality in its models.

Leader in generative enterprise search technology

Considered one of the unicorns of the emerging generative AI scene, Glean provides AI-powered search that primarily focuses on workplace and enterprise knowledge bases. With its Workplace Search, Assistant, Knowledge Management, Work Hub, and Connectors features, business leaders can set up a self-service learning and resource management tool for employees to find important documentation and information across business applications and corporate initiatives.

Commitment to general intelligence AI assistants

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150 Top AI Companies of 2024: Visionaries Driving the AI Revolution - eWeek

Will we see artificial intelligence jobs for MBAs? Find out which MBA jobs AI could revolutionize or replaceand what new opportunities await grads who know how to use AI tools

Like every technological shift, this change brings uncertainty. Many functions look poised to be drastically changed, reduced, or even replaced by AI. Some particular areas where changes are expected are in customer service, proofreading, and bookkeeping.

But what impact will AI have on MBA jobs? Are the career paths most suited to MBAs going to be shut off or rerouted? Or, as the World Economic Forum predicts, will the new technology create more avenues than it closes?

We spoke to AI experts at several top business schools, including Dartmouth College's Tuck School of Business, The Wharton School, and Carnegie Mellon University's Tepper School of Business to find out how AI will affect MBA jobs. Heres what they said.

The MBA degree is designed to prepare students for roles in middle management and higher. With this in mind, MBAs are insulated from some of the immediate changes AI is making.

Right now, we anticipate that AI will have the biggest impact on early career and entry-level roles that our students will oversee in more managerial positions, says Joe Hall, senior associate dean for teaching and learning at the Tuck School of Business at Dartmouth.

However, this does mean that managers must know how to manage changes in subordinates roles and understand how AI tools work.

While AI may not have a massive effect on Tuck MBAs day-to-day lives just yet, they undoubtedly need to understand how this technology works and why it has such great potential, says Joe.

To prepare students, the Tuck school has introduced a slate of new courses focused on AI, including a module specifically addressing AI for managers.

Where AI will make a direct impact on MBA jobs is as a managerial tool.

Eric Bradlow, vice dean of AI and analytics at Wharton, says: AI will clearly create jobs that require workers, managers, and the C-suite to use AI as a decision support tool."

That is, there will need to be workers who know how to utilize AI, bring AI to their organization, lead in a world of AI, train people in AI, and so on. In addition, a number of new industries will arise because of AI.

MBA grads will need to know how to use AI to support decision-making by using it to quickly verify or aggregate data, spot trends, or assess risks.

The Wharton School hasnt wasted any time in preparing students with these AI skills, Eric adds.

We are providing ChatGPT licenses to all MBA students. We will be providing training courses in AI. We haveAI Hack-a-thonswhich reward the best student ideas in AI. We have experiential learning projects that allow students to apply AI to real companies.

The school also recently launched the Wharton AI & Analytics Initiative to harness AI for four groups: industry, researchers, students, and society at large.

So far, weve seen that AI will change the roles that MBAs manage and provide a useful tool for helping them do so. It will also, crucially, accentuate the value of their human skills.

What we can say with a degree of certainty is that in any future jobs we will shift, where appropriate, to a collaboration between workers and AI, says Laurence Ales, professor of economics and GenAI fellow at the Tepper Schools Center for Intelligent Business.

Beyond knowledge of how to interact with AI, this shift will also require workers to be comfortable with higher-level thinking: workers should be able to understand the problems and be able to formulate the right questions more than executing routine tasks to reach the answer.

This type of critical thinking is an essential skill in an AI-enabled workplaceand one that business schools are well-equipped to provide.

At the Tepper school we recognize that decision-making requires a framework to understand problems, says Laurence.

Our program helps students build actionable frameworks in all of the functional areas of business. The strategic, analytical, and leadership skills of our MBA students will be crucial in navigating AI-driven transformations and driving business growth.

Most MBA graduates go into one of three industries after their degree: consulting, finance, or technology. At Wharton, for example, over 86% of the most recent class took up roles in one of these three industries.

How will each of these industries be impacted by AI? Heres our snapshot.

AI is unlikely to replace human consultants, for many of the same reasons that it will not replace human managers across industries. Current AI models cannot replace the combination of creativity and strategic thinking offered by human consultants.

However, AIs potential as a decision support tool comes in especially handy in consulting, for example, fast-tracking data analysis and automating other tasks.

If youre an aspiring MBA consultant, look out for MBA programs that address the need for AI skills in consulting, like the program at Dartmouth Tuck.

Many Tuck students enter the consulting field after graduation and we recognized a need for a course specifically about AIs impact on consulting, says Joe Hall.

So, we introduced AI and Consultative Decision-Making, which serves as a hands-on laboratory to give students experience using generative AI in an advisory and decision-making capacity.

AI will likely make sweeping changes in financial servicesagain, mainly as a decision-support tool. Its likely functions include:

Making transaction processing faster

Automating fraud detection

Analyzing asset performance

Assessing risk for insurance purposes

It can even help on the compliance side of finance by quickly analyzing complex regulations and legal texts.

AI will make broad changes in the tech industry, chiefly as a streamlining tool. One example is in software development, where AI can test and even generate new code.

It also has big implications for cybersecurity. An AI arms race is emerging between cyber attackers, who may use AI to create relentless and constantly-evolving threats, and cyber defenders, who can use the technology to create evolving defenses.

As such, there may be fruitful opportunities for MBAs with interests in cybersecurity and AI to pioneer in this field.

Most MBA jobs are somewhat protected from replacement by artificial intelligence by being management-focused. Humans creativity and strategic thinking skills are still irreplaceable.

However, that doesnt mean that MBAs can ignore the impact of AI, or put off learning about it. To exercise irreplaceable higher-order thinking skills and manage in the world of artificial intelligence, MBAs should learn how to apply AI tools to decision-making and keep abreast of technological developments.

Additionally, MBAs may have opportunities to forge careers on the frontlines of new industries or sectorsfor instance, managing cybersecurity in the age of AI.

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How Will Artificial Intelligence Change MBA Jobs? - BusinessBecause

Attention around artificial intelligence has driven chipmaker Nvidia sharply higher in recent years, to the point where it briefly was the worlds most valuable company but AI investors are targeting other stocks, too.

Some hardware-focused companies in the AI supply chain have seen blistering stock price gains in the past 18 months, and the process of implementing AI across large organizations is already driving business for leading software firms.

The hype has drawn comparisons to the dot-com bubble of the late 1990s, when many internet start-ups saw massive, short-lived investment gains before crashing down. But this time, some analysts say, much of the AI interest has been concentrated on a much smaller number of established technology firms, and it is linked to significant corporate spending happening now.

The impact of generative AI is not as broad-based as initially imagined, said Chirag Dekate, a vice president and analyst at Gartner. There are a very specific entities that are providing the foundational technology.

Here are some publicly traded companies riding a wave of artificial intelligence investment.

Co-founded by technology investor Peter Thiel, this company has evolved from an organization doing mostly defense and intelligence work into a data company serving enterprises of all sorts. Under chief executive Alex Karp, the company has built a growing suite of artificial-intelligence offerings.

It is among a growing industry that implements AI technology for large organizations, a sector that also includes C3AI and the consulting firms Deloitte, Accenture and Ernst & Young, according to Dekate.

Palantirs platform examines a companys data and provides examples of how AI can be employed within an organization. Wedbush Securities analyst Dan Ives said he sees Palantir as the golden child of AI because of its emphasis on the practical use of artificial intelligence within large organizations.

Nvidia chips are just the start, but it all comes down to use cases, Ives said.

The contract manufacturing giant has a ubiquitous presence in the global tech industry with its production of computer chips built into consumer products like smartphones and cars, as well as military satellites and weapons systems.

Deepwater Assets Munster says his firm is invested in TSMC, along with Broadcom and Vertiv, as part of a broader play to capitalize on the growth of AI-enabling hardware. A company called Onto Innovation, which handles specialized measurement for chip construction, is also seen as a niche beneficiary.

Hardware is the play right now because were seeing tangible improvements to their business. They are trading at software-like multiples, Munster said. The hardware for this is just getting built.

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It's not just Nvidia: AI interest sends these stocks higher - The Washington Post

Miami, FL, June 27, 2024 (GLOBE NEWSWIRE) -- CryptoHeap, a leading name in the cryptocurrency staking industry, is excited to announce its latest innovation: AI-driven crypto staking. By leveraging cutting-edge artificial intelligence, CryptoHeap aims to revolutionize efficiency and profitability in cryptocurrency staking, setting a new standard for the industry. This groundbreaking technology is poised to enhance user experience, optimize returns, and solidify CryptoHeaps position as one of the best crypto staking platforms available.

Salvage Warwick, CEO of CryptoHeap, highlighted the transformative potential of AI in crypto staking. "The integration of AI into our staking platform is a significant milestone for CryptoHeap. This advancement allows us to provide users with more efficient, accurate, and profitable staking opportunities. We believe AI-driven staking will be a game-changer, not just for our platform, but for the entire industry," Warwick stated.

Enhancing Efficiency with AI

Artificial intelligence offers numerous benefits forcrypto staking platforms. By employing machine learning algorithms and predictive analytics, CryptoHeap can process vast amounts of market data in real-time. This capability enables the platform to make informed decisions, optimize staking strategies, and maximize returns for users. The AI-driven approach also improves risk management, providing investors with a more secure and stable staking experience.

"Our AI-driven platform continuously learns and adapts to market conditions. This means our users benefit from the most current strategies and insights, making their staking experience more rewarding and secure," Warwick explained.

Comprehensive Staking Packages

CryptoHeaps AI-driven platform offers a range of staking packages tailored to various investment goals. These packages include some of thebest crypto stakingcoins, positioning CryptoHeap as a top choice for those looking to invest in the best crypto to stake in 2024. By providing options with daily rewards, capital return, and significant referral bonuses, CryptoHeap ensures a diverse range of opportunities for investors.

Focus on Ethereum Staking

Ethereum remains a focal point for many investors, and CryptoHeap's AI-driven platform offers some of the best ethereum staking platforms available. The platforms advanced AI capabilities provide enhanced insights and strategies for staking Ethereum, ensuring users can maximize their returns safely and efficiently.

Warwick emphasized the benefits of Ethereum staking on the platform. "Ethereum staking is a cornerstone of our offerings. Our AI technology provides users with the best possible strategies for staking Ethereum, addressing common concerns such as 'is staking ethereum a good idea' and 'is staking ethereum safe.' With our platform, users can stake Ethereum with confidence and achieve superior returns," he said.

Comprehensive Staking Packages

CryptoHeap offers a diverse range of staking packages, each tailored to meet various investment needs. These packages include options for some of the best crypto staking coins, making CryptoHeap one of thebest crypto staking platformsin the market. Investors can choose from staking options that offer daily rewards, capital return, and significant referral bonuses.

Warwick emphasized the platform's commitment to providing the best staking crypto options, particularly highlighting Ethereum staking. "Ethereum staking remains one of the most popular choices among our users. We offer some of the best ethereum staking platforms, ensuring that our users can stake their ETH safely and profitably. For those asking 'is staking ethereum a good idea' and 'is staking ethereum safe,' we provide robust solutions that address these concerns," he explained.

Strategic Monitoring and Future Plans

As the crypto market evolves, CryptoHeap remains committed to innovation and user satisfaction. The platform continuously enhances its AI capabilities to ensure users can navigate the complexities of the crypto market effectively.

"We are continuously improving our AI algorithms and expanding our offerings to meet the needs of our users. Our focus on innovation and excellence ensures CryptoHeap remains at the forefront of the crypto staking industry," Warwick concluded.

With the introduction ofAI-driven crypto staking, CryptoHeap is set to revolutionize the industry. The platforms commitment to leveraging cutting-edge technology, providing comprehensive staking packages, and ensuring security and education positions it as a leader in the crypto staking space.

Investors and crypto enthusiasts are encouraged to explore the AI-driven staking packages and other features available on CryptoHeaps platform. For more information about CryptoHeaps services and upcoming enhancements, visit the official website athttps://cryptoheap.com/.

Disclaimer: The information provided in this press release is not a solicitation for investment, nor is it intended as investment advice, financial advice, or trading advice. It is strongly recommended you practice due diligence, including consultation with a professional financial advisor, before investing in or trading cryptocurrency & securities.

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Leveraging Artificial Intelligence to Revolutionize Efficiency in Cryptocurrency Staking - GlobeNewswire

Todays world witnesses exceptional advances in technology, positioning Artificial Intelligence (AI) to revolutionise various aspects, including mental health interventions. With its ability to analyse vast data sets, detect intricate patterns and generate practical insights, AI offers the potential for transforming mental healthcare. This could lead to personalised, accessible and effective interventions.

From early issue detection to customised treatment plans and 24/7 virtual support, AI-driven solutions reshape mental wellness approaches. This article delves into six important ways AI is reshaping mental health therapies. It emphasises technologys transformational potential for assisting qualified professionals who have completed counselling courses and other requisite degrees in boosting holistic well-being and improving the lives of individuals suffering from mental illnesses.

AI has the astonishing capacity to handle massive volumes of data. This skill enables the development of highly tailored treatment regimens for individuals dealing with mental health issues. By analysing a patients genetic information, medical history, lifestyle factors and social determinants of health, AI algorithms can identify unique patterns and correlations.

These insights inform tailored treatment strategies. For instance, AI can determine the optimal combination of therapies, medications and lifestyle changes most likely to yield positive outcomes for a specific individual. Moreover, AIs continuous learning and adaptation ensure dynamic adjustments to treatment plans based on the patients response and evolving needs. This tailored strategy increases treatment efficacy, reduces unpleasant effects and ineffective therapies, and, eventually, raises overall care quality.

The application of artificial intelligence in therapy sessions enhances the process of receiving therapy by providing critical insights into clients emotional states and actions. AI systems can identify tiny signals and nuances that humans cannot. AI can detect changes in facial expressions indicative of underlying emotions like sadness, anger, or anxiety, allowing therapists to tailor interventions accordingly. AI-powered sentiment analysis gauges interactions tone and mood, facilitating deeper empathy and rapport between therapists and clients. AI helps therapists improve their observational skills and emotional intelligence, resulting in more nuanced and successful therapy treatments that promote better client understanding, self-awareness and emotional growth.

Artificial Intelligence technology transforms mental healthcare through in-depth data exploration. AI systems compile and analyse anonymised patient information from electronic records, wearable devices, social platforms and other sources. These algorithms detect trends, patterns and risk factors linked to various psychological conditions. For example, AI could uncover connections between specific genetic markers and treatment effectiveness or between environmental stressors and symptom worsening. Such insights drive the development of tailored treatments matching personal needs and circumstances.

Furthermore, AI offers predictive modelling, which enables clinicians to identify and avert approaching mental health crises. By embracing data-driven insights, mental health practitioners may enhance treatment outcomes, save healthcare costs and promote overall community well-being.

AI can help aid mental health experts in proactive strategies. These strategies prevent the onset or recurrence of mental health crises. AI algorithms analyse an individuals past data. This includes treatment outcomes, medication adherence, lifestyle factors and environmental stressors.

AI can identify patterns and triggers indicating potential relapse or declining mental health. For instance, AI may recognise early warning signs like changes in sleep, social withdrawal or mood fluctuations. This prompts timely interventions such as coping strategies, medication adjustments, lifestyle changes or targeted support services.

Proactive interventions can mitigate symptom severity, enhance resilience and improve long-term prognosis. Furthermore, AI facilitates continuous monitoring and feedback, allowing the refinement of preventative interventions based on real-time data and patient feedback. This proactive strategy promotes individual well-being and helps to provide long-term, cost-effective mental healthcare services.

AI technology enhances mental health therapies, reducing the stigma associated with seeking assistance. Many people are hesitant to use traditional services because they are afraid of being judged or discriminated against. AI tools like chatbots and virtual therapists offer confidential, non-judgemental spaces to freely express thoughts, feelings and concerns without stigmas shadow. These AI-based tools are accessible anytime, anywhere, providing discreet, convenient support channels.

Moreover, anonymity allows sensitive disclosures and taboo topic discussions without social repercussions. AI interventions are often perceived as impartial and objective, devoid of human biases, which can reassure those apprehensive about unfair treatment or misunderstanding from professionals. By fostering trust and confidentiality, AI mental health platforms encourage more people to seek help, destigmatising support-seeking and normalising mental health conversations. As people interact with AI mental health tools, they may develop greater comfort in discussing concerns with human professionals, further reducing stigma in healthcare settings.

Ultimately, AI breaks access barriers and cultivates an accepting, supportive environment for those struggling with mental health issues.

Mental health issues can sometimes go unrecognized until they become serious. Fortunately, AI-powered solutions have proven helpful for early detection. By analysing data from various sources like social media, smartphone usage and wearable devices, AI algorithms can identify subtle changes that may indicate mental health concerns. For example, increased social isolation, altered communication patterns or irregular sleep habits could signal depression or anxiety. These deviations from typical behaviour serve as early warning signs.

AI sentiment analysis is also remarkably useful. By examining written text or speech, these algorithms can detect emotional cues and linguistic markers linked to mental health symptoms. The tone, sentiment, and context of communications are scrutinised, with patterns of negative language, hopelessness or self-harm references triggering alerts for further assessment. This proactive approach enables timely intervention before symptoms escalate.

Early detection facilitated by AI allows for prompt support and treatment, preventing symptoms from worsening and improving outcomes. When mental health issues are identified early, clinicians can implement targeted interventions like psychoeducation, counselling or referrals to specialised services. This encourages people to seek care proactively, establishing a sense of agency in controlling their mental health. By utilising AI for early diagnosis, mental healthcare may shift to a preventative approach, with resilience and early intervention as important pillars.

AIs integration into mental healthcare opens doors for ground-breaking solutions, yet we must prioritise ethical practices. Ensuring privacy, fairness and human dignity should guide technological progress. Collaborative efforts between developers, healthcare providers, and individuals with lived experiences can create inclusive, equitable and impactful mental health interventions. Accepting AIs revolutionary potential ethically and compassionately opens the way for a future in which the mind stays at the forefront.

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The Impact Of Artificial Intelligence On Mental Health Interventions - Dataconomy

This article was originally published for the German Federal Foreign Offices Artificial Intelligence and Weapons of Mass Destruction Conference 2024, held on the 28th of June, and can be read here. You can also read The implications of AI in nuclear decision-making, by ELN Policy Fellow Alice Saltini, who will be speaking on a panel at the conference.

Artificial intelligence (AI) is a catalyst for many trends that increase the salience of nuclear, biological or chemical weapons of mass destruction (WMD). AI can facilitate and speed up the development or manufacturing of WMD or precursor technologies. With AI assistance, those who currently lack the necessary knowledge to produce fissile materials or toxic substances can acquire WMD capabilities. AI itself is of proliferation concern. As an intangible technology, it spreads easily, and its diffusion is difficult to control through supply-side mechanisms, such as export controls. At the intersection of nuclear weapons and AI, there are concerns about rising risks of inadvertent or intentional nuclear weapons use, reduced crisis stability and new arms races.

To be sure, AI also has beneficial applications and can reduce WMD-related risks. AI can make transparency and verification instruments more effective and efficient because of its ability to process immense amounts of data and detect unusual patterns, which may indicate noncompliant behaviour. AI can also improve situational awareness in crisis situations.

While efforts to explore and exploit the military dimension of AI are moving ahead rapidly, these beneficial dimensions of the AI-WMD intersection remain under-researched and under-used.

The immediate challenge is to build guardrails around the integration of AI into the WMD sphere and to slow down the incorporation of AI into research, development, production, and planning for nuclear, biological and chemical weapons. Meanwhile, governments should identify risk mitigation measures and, at the same time, intensify their search for the best approaches to capitalise on the beneficial applications of AI in controlling WMD. Efforts to ensure that the international community is able to govern this technology rather than let it govern ushave to address challenges at three levels at the AI and WMD intersection.

First, AI can facilitate the development of biological, chemical or nuclear weapons by making research, development and production faster and more efficient. This is true even for old technologies like fissile material production, which remains expensive and requires large-scale industrial facilities. AI can help to optimise uranium enrichment or plutonium separation, two key processes in any nuclear weapons programme.

The connection between AI and chemistry and biochemistry is particularly worrying. The Director General of the Organisation for the Prohibition of Chemical Weapons (OPCW) has warned of the potential risks that artificial intelligence-assisted chemistry may pose to the Chemical Weapons Convention and of the ease and speed with which novel routes to existing toxic compounds can be identified.This creates serious new challenges for the control of toxic substances and their precursors.

Similar concerns exist with regard to biological weapons. Synthetic biology is in itself a dynamic field.But AI puts the development of novel chemical or biological agents through such new technologies on steroids. Rather than going through lengthy and costly lab experiments, AI can predict the biological effects of known and even unknown agents. Amuch-cited paper by Filippa Lentzos and colleaguesdescribes an experiment during which an AI, in less than six hours and running on a standard hardware configuration, generated forty thousand molecules that scored within our desired threshold, meaning that these agents were likely more toxic than publicly known chemical warfare agents.

Second,AI could ease access to nuclear, biological and chemical weapons by illicit actors by giving advice on how to develop and produce WMD or relevant technologies from scratch.

To be sure, current commercial AI providers have instructed their AI models not to answer questions on how to build WMD or related technologies. But such limits will not remain impermeable. And in future, the problem may not be so much preventing the misuse of existing AI models but the proliferation of AI models or the technologies that can be used to build them. Only a fraction of all spending on AI is invested in the safety and security of such models.

Third, the integration of AI into the WMD sphere can also lower the threshold for the use of nuclear, biological or chemical weapons. Thus, all nuclear weapon stateshave begun to integrate AI into their nuclear command, control, communication and information (NC3I) infrastructure. The ability of AI models to analyse large chunks of data at unprecedented speedscan improve situational awareness and help warn, for example, of incoming nuclear attacks. But at the same time AI may also be used to optimise military strike options. Because of the lack of transparency around AI integration, fears that adversaries may be intent on conducting a disarming strike with AI assistance can increase, setting up a race to the bottom in nuclear decision-making.

In a crisis situation, overreliance on AI systems that are unreliable or working with faulty data may create additional problems. Data may be incomplete or may have been manipulated. AI models themselves are not objective. These problems are structural and thus not easily fixed.A UNIDIR study, for example, found that gender norms and bias can be introduced into machine learning throughout its life cycle. Another inherent risk is that AI systems designed and trained for military uses are biased towards war-fighting rather than war avoidance, which would make de-escalation in a nuclear crisis much more difficult.

The consensus among nuclear weapons states that a human always has to stay in the loop before a nuclear weapon is launched, is important, but it remains a problem that the understanding of human control may differ significantly.

It would be a fools errand to try to slow down AIs development. But we need to decelerate AIs convergence with the research, development, production, and military planning related to WMD. It must also be possible to prevent spillover from AIs integration into the conventional military sphere to applications leading to nuclear, biological, and chemical weapons use.

Such deceleration and channelling strategies can build on some universal norms and prohibitions. But they will also have to be tailored to the specific regulative frameworks, norms and patterns regulating nuclear, biological and chemical weapons. Thezero draft of the Pact for the Future, to be adopted at the September 2024Summit of the Future, points in the right direction by suggesting a commitment by the international community to developing norms, rules and principles on the design, development and use of military applications of artificial intelligence through a multilateral process, while also ensuring engagement with stakeholders from industry, academia, civil society and other sectors.

Fortunately, efforts to improve AI governance on WMD do not need to start from scratch. At the global level, the prohibitions of biological and chemical weapons enshrined in the Biological and Chemical Weapons Conventions are all-encompassing: the general purpose criterion prohibits all chemical and biological agents that are not used peacefully, whether AI comes into play or not. But AI may test these prohibitions in various ways, including by merging biotechnology and chemistry seamlessly with other novel technologies. It is, therefore, essential the OPCW monitors these developments closely.

International Humanitarian Law (IHL) implicitly establishes limits on the military application of AI by prohibiting the indiscriminate and disproportionate use of force in war. The Group of Governmental Experts (GGE) on Lethal Autonomous Weapons under the Convention on Certain Conventional Weapons (CCW)is doing important work by attempting to spell out what the IHL requirements mean for weapons that act without human control. These discussions will,mutatis mutandis, also be relevant for any nuclear, biological or chemical weapons that would be reliant on AI functionalities that reduce human control.

Shared concerns around the risks of AI and WMD have triggered a range of UN-based initiatives to promote norms around responsible use. The legal, ethical and humanitarian questions raised at the April 2024Vienna Conference on Autonomous Weapons Systems are likely to inform debates and decisions around limits on AI integration into WMD development and employment, and particularly nuclear weapons use. After all, similar pressures to shorten decision times and improve the autonomy of weapons systems apply to nuclear as well as conventional weapons.

From a regulatory point of view, it is advantageous that the market for AI-related products is still highly concentrated around a few big players. It is positive that some of the countries with the largest AI companies are also investing in the development of norms around responsible use of AI. It is obvious that these companies have agency and, in some cases, probably more influence on politics than small states.

TheBletchley Declarationadopted at the November 2023 AI Safety Summit in the UK, for example, highlighted the particular safety risks that arise at the frontier of AI. These could include risks that may arise from potential intentional misuse or unintended issues of control relating to alignment with human intent. The summits on Responsible Artificial Intelligence in the Military Domain (REAIM) are anothereffort at coalition building around military AI that could help to establish the rules of the game.

ThePolitical Declaration on Responsible Military Use of Artificial Intelligence and Autonomy, agreed on in Washington in September 2023, confirmed important principles that also apply to the WMD sphere, including the applicability of international law and the need to implement appropriate safeguards to mitigate risks of failures in military AI capabilities. One step in this direction would be for the nuclear weapon states to conduct so-called failsafe reviewsthat would aim to comprehensively evaluate how control of nuclear weapons can be ensured at all times, even when AI-based systems are incorporated.

All such efforts could and should be building blocks that can be incorporated into a comprehensive governance approach. Yet, the risks around AI leading to increased risk of nuclear weapons use are most pressing. Artificial intelligence is not the only emerging and disruptive technology affecting international security.Space warfare, cyber, hypersonic weapons, and quantum are all affecting nuclear stability. It is, therefore, particularly important that nuclear weapon states amongst themselves build a better understanding and confidence about the limits of AI integration into NC3I.

An understanding between China and the United States on guardrails around military misuse of AI would be the single most important measure to slow down the AI race. The fact that Presidents Xi Jinping and Joe Biden in November 2023 agreed that China and the United States have broad common interests, including on artificial intelligence, and to intensify consultations on that and other issues, was a much-needed sign of hope. Although since then China has been hesitating to actually engagein such talks.

Meanwhile, relevant nations can lead by example when considering the integration of AI into the WMD realm. This concerns, first of all, the nuclear weapon states which can demonstrate responsible behaviour by pledging, for example, that they would not use AI to interfere with the nuclear command, control and communication systems of their adversaries. All states should also practice maximum transparency when conducting experiments around the use of AI for biodefense activities because such activities can easily be mistaken for offensive work. Finally, the German governments pioneering role in looking at the impact of new and emerging technologies on arms control has to be recognised. Its Rethinking Arms Control conferences, including the most recent conference on AI and WMD on June 28 in Berlin with key contributors such as the Director General of the OPCW, are particularly important. Such meetings can systematically and consistently investigate the AI-WMD interplay in a dialogue between experts and practitioners. If they can agree on what guardrails and speed bumps are needed, an important step toward effective governance of AI in the WMD sphere has been taken.

The opinions articulated above represent the views of the author(s) and do not necessarily reflect the position of the European Leadership Network or any of its members. The ELNs aim is to encourage debates that will help develop Europes capacity to address the pressing foreign, defence, and security policy challenges of our time.

Image credit: Free ai generated art image, public domain art CC0 photo. Mixed with Wikimedia Commons / Fastfission~commonswiki

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The fast and the deadly: When Artificial Intelligence meets Weapons of Mass Destruction - European Leadership Network

IDAHO FALLS Artificial intelligence is transforming the way the nuclear industry works, and Idaho National Laboratory is leading the way developing applications to streamline processes while improving safety at nuclear power plants.

INL scientists showcased 15 projects on Artificial Intelligence (AI) and Machine Learning at an expo at the Energy Innovation Laboratory in Idaho Falls on Tuesday.

Were here to learn about some of the incredible science happening related to artificial intelligence and machine learning, said Katya Le Blanc, human factors scientist at Idaho National Laboratory. Were also developing technologies that can eventually be deployed by the nuclear industry and be used by nuclear utilities.

According to a lab news release, computers that mimic cognitive functions and apply advanced algorithms can help researchers analyze and solve a variety of complex technical challenges. This new approach helps everything from improving materials design for advanced reactors to enhancing nuclear power plant control rooms so they become more effective and efficient.

Technologies on display at the conference included RAVEN a Risk Analysis Virtual ENvironment that provides an open source multi-purpose framework for machine learning, artificial intelligence and digital twinning.

One machine learning technology called Inspection Portal is part of the light water reactor sustainability program that analyzes and aggregates data from human-submitted reports to identify trends and help optimize the operation of nuclear power plants.

The programs machine learning operation is trained on millions of records from across the industry.

We can do things here at the INL that no one else can do, said Brian Wilcken, nuclear science and technology data scientist. Utility companies try to do things like this. They cant touch it. We have so much data we can train extremely powerful models.

Other AI systems provide image detection to read gauges, pinpoint anomalies and determine if a valve has been turned, if a screw is corroded or if a fire breaks out in a nuclear plant. These advancements could reduce the need for personnel to perform menial checks at a nuclear power plant and free up manpower for higher-level work and applications.

Additional tools evaluate the economics of different energy mixes and how to analyze the best cost-benefit and other factors (such as) the reliability associated with energy systems, Le Blanc said.

These systems can determine the proper output needed from a nuclear power plant, a hydro plant and a solar facility in order to meet peoples demand for electricity when they need it while optimizing economic benefit as well, she said.

Some of the applications utilize existing AI programs, while others were created in-house at Idaho National Laboratory.

Sometimes, it requires that you develop it. Theres not a model that can do what you need it to do, but sometimes theres something that already exists that you can adapt, Le Blanc said. It varies depending on (the situation), but theres no reason to start from scratch.

The Artificial Intelligence and Machine Learning Expo is in its second year.

In the future, organizers hope to expand and collaborate with other experts in the AI space to further share the research occurring at Idaho National Laboratory.

I read a lot of papers inside scientific journals related to AI, Le Blanc said. Seeing how this stuff actually works, being able to mess around with it, play with it, talk to the researchers, see what theyre doing and get direct access and ask them questions thats just exciting!

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AI goes nuclear: INL expo showcases machine learning and artificial intelligence - East Idaho News

Experts gather in the Vatican to discuss the ethical and anthropological implications of Artificial Intelligence, emphasising the need for regulation and responsible use of data.

By Michele Raviart and Francesca Merlo

After Pope Francis message was released for World Communications Day last month, a conference entitled The algorithm at the service of humankind. Communicating in the age of artificial intelligence took place in the Vatican on Thursday, 27 June, gathering experts in the fields of AI and communications to compare ideas and discuss concerns on the issue.

Within the Casina Pio IV walls, the conference addressed questions such as those posed by Dr Paolo Ruffini, Prefect of the Dicastery for Communication, as he gave the opening remarks. He asked: Artificial intelligence translates everything into calculation, but can we reduce everything to a statistical probability? How can we protect professionals and workers in the media from the arrival of AI and maintain the right to inform and be informed on the basis of truth, freedom and responsibility? How can we make large platforms that invest in generative AI interoperable so that they do not reduce humans to a reservoir of data to be exploited?

Welcoming the participants along with Dr Paolo Ruffini was Fr Lucio Ruiz, Secretary of the Dicastery for Communication, who highlighted some of what Pope Francis has said concerning the theme of Artificial Intelligence. He emphasized that the Pope's interventions on Artificial Intelligence demonstrate the Church's "intuition" in walking with humanity through its culture and historical changes. He explained that this was the case 500 years ago when the first Vatican printing press was created - shortly after Gutenberg's discovery. Likewise, it was proved with the construction of Vatican Radio by the inventor of the radio himself, Guglielmo Marconi, in 1931. And another example, he added, is the creation of the vatican.va portal in 1994, when the web had only just begun to appear on people's computers.

The next person to speak was Father Paolo Benanti, professor of ethics and bioethics at the Pontifical Gregorian University, president of the AI Commission for Information, and member of the United Nations AI Committee. He opened the first of two panel discussions on "The Ethics of Algorithms and the Challenges for Communication." Fr Benanti began by highlighting the primary essence of computers, which is to perform calculations. Benanti recalled how the invention of transistors, made available by the United States to its allies after the successes of World War II, changed reality. Early computer prototypes contributed to the discovery of the atomic bomb and the decoding of secret codes used by Nazi Germany. From that centralised vision of technology, and through the revolution led by Silicon Valley pioneers in the 1970s, he noted that we eventually arrived at a "personal" and intimate computation, first through PCs and then smartphones. With ChatGPT and its implementation in Apple and Microsoft phone interfaces, he emphasised that we still do not know how much of the computational power will be personal and how much will be centralised in the cloud. Therefore, he stressed that regulation is necessary, as the European Union has done, to manage artificial intelligence in the same way traffic laws have been established for cars.

Also speaking at the conference was Nunzia Ciardi, Deputy Director General of the National Cybersecurity Agency. She said that Artificial Intelligence is not an impressive technological leap in itself. What makes its implementation something that will have a decisive anthropological impact on reality, she explained, is the fact that it relies on an enormous amount of data collected "brutally" over the decades by companies through free services or applications that have become essential for us. Ciardi highlighted other aspects, such as the use of the English language to train algorithms with all the values and cultural expressions that one language carries compared to another and the risk of increasingly struggling to decode complex messages, which can be dangerous in a democracy.

Professor Mario Rasetti, Emeritus of Theoretical Physics at the Polytechnic University of Turin and President of the Scientific Board of CENTAI, also spoke at the conference. He commented that "knowledge is becoming private property," recounting the experience of OpenAI, which started as a non-profit organisation of scientists and was acquired by Microsoft for $10 billion. Rasetti added that we must make Artificial Intelligence a science with rigorous definitions because, in its current state, it presents itself as a probabilistic tool, which can hardly measure intelligence, truth, and causality.

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The algorithm at the service of humankind: Communicating in the age of AI - Vatican News - English

Artificial intelligence lies.

Everyone knows this by now, of course. Programs such as ChatGPT and Googles AI overviews routinely generate nonsense when queried by users. Tech enthusiasts call these mistakes hallucinations, as though AI just needs to sober up and come to its senses. I dont see it that way.

Because AI has started fibbing about me and my family.

Last week, my husband received a spam email from a salesman. It included a history of our last name, as follows:

The last name Wirestone is believed to have originated in Germany. It is a locational surname, meaning it was likely given to individuals based on where they lived. The name Wirestone may have derived from a place name that no longer exists or has changed over time.

The surname Wirestone first appeared in records in the late 19th and early 20th centuries in the United States, with immigrants from Germany bringing the name over. Some variations of the surname include Wierstien, Wierstone, and Wierston.

Today, the surname Wirestone is relatively rare and is primarily found in the United States. Individuals with this last name can be found in various states across the country, but they are most concentrated in the Midwest region.

The only problem with this account is that it is entirely incorrect.

I know this firsthand because the last name Wirestone didnt exist before 2010, when my husband and I made it up. We took the letters from our original last names and arranged them to create a new one. We also considered Cointower and McWren as options.

At the time, we researched to make sure that no one else had the last name of Wirestone. No one did. A marketing company bore the name Wire Stone, but that seemed sufficiently separate for our purposes. We lived in New Hampshire at the time, and the state had just legalized same-sex marriage. We wanted to share a single last name, and we wanted to share that last name with our son.

I even wrote a column mentioning this back in 2013! (Yes, Ive been churning out copy for a long time.)

But when it comes to large language models, the facts dont matter.

The email my husband received looked like the work of ChatGPT to me, so I headed over and put that AI through its paces. Sure enough, it generated loads of lies about my last name, all of them along the same lines. Heres a paragraph from one, this time including a linguistic breakdown:

The last name Wirestone is not as common as some others, but it does have a history rooted in Germanic origins. Wire likely comes from the Middle High German word wir, meaning wire or metal, indicating a possible occupational origin for individuals who worked with wire or metal. Stone suggests a connection to a place or geographical feature, possibly indicating someone who lived near a notable stone or rocky area.

Sounds authoritative! Also, completely false.

You might ask how AI generates something so completely bananas. Its because AI cant tell the difference between true and false. Instead, a complex computer program plays probabilistic language guessing games, betting on what words are most likely to follow other words. If an AI program hasnt been trained on a subject unusual last names, for instance it can conjure up authoritative-seeming but false verbiage.

ChatGPT later spawned a different etymologyfor our last name:

The surname Wirestone appears to have German origins. It is derived from the Old High German name Wiro, which means warrior or army, and stein, which means stone. Thus, the surname Wirestone likely originated as a combination of these elements, possibly indicating someone who was strong like a stone in battle or had characteristics associated with a warrior.

To summarize: My ancestors were either metalworkers who lived near rocky outcroppings or toughened fighters.

You might dismiss this all as mere silliness. I would agree with you, except that leaders have decided over the past year that AI will transform the global economy.

Google, which has become the default source of definitive world knowledge, began employing AI in its search results. Users soon reported that Google was telling them tosmoke cigarettes while pregnant, add glue to theirhome-baked pizza,sprinkle used antifreezeon their lawns, and boil mint in order tocure their appendicitis, according to Slate. The company has since rolled back some of the changes.

Facebook has tacked gaudy AI features across the platform. In the meantime, it managed to block Kansas Reflector and remove every link we had ever posted. Users who attempt to share our stories still report problems doing so, even though we were assured in April by spokesman Andy Stone that the problem had been corrected.

All the while, OpenAI, the company behind ChatGPT, continues to raise money and investor expectations ever higher about the future of its technology.

Yet were not living in the future. Were living in the now, and AI has massively underperformed in every instance where users asked it to perform accurately and reliably. Writing blender instructions in the style of the King James Bible is a fun party trick. But folks turn to the internet to answer real, pressing questions about their world.

I can tell you firsthand, from information I know personally, that the technology does not deliver.

Ten years ago, if you searched Google for information about my last name, you would find links to my work, the marketing company and the column I had written. You would be able to figure out the truth of the situation.

Now, that column has fallen prey to link rot. Those curious about Wirestone may well turn to ChatGPT, as students have done since the technology made its debut. They will be fed lies. The experience of a curious person online has therefore degraded, not improved. Perhaps AI technology will improve in the months and years to come. Perhaps not.

In the meantime, treat the output of opaque AI systems with extreme skepticism. Follow actual news reported and written and edited by actual humans. Visit Kansas Reflectors website. Subscribe to our newsletter.

Focus on reality, and leave the hallucinations behind.

Clay Wirestone is Kansas Reflector opinion editor. Through its opinion section, Kansas Reflector works to amplify the voices of people who are affected by public policies or excluded from public debate. Find information, including how to submit your own commentary, here.

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Artificial intelligence has spread lies about my good name, and I'm here to settle the score Kansas Reflector - Kansas Reflector

Over the past two years, artificial intelligence (AI) has been the key trend that many investors have focused on. The amount of technological innovation with AI has caused waves among users everywhere. Most have tried out ChatGPT or other generative AI models and come to the same conclusion: AI is smart and is certainly going to be a resource we all utilize moving forward.

Questions around how AI will be used aside, certain companies are uniquely poised to benefit from the surge in AI application growth over time. These three long-term AI stocks may not be surprising to many. Im focusing on the best of the best in this sector in this piece. However, its worth noting that quality matters in this space. In my view, these are the three companies with sustainable AI tailwinds I think the market is right to focus on right now.

Source: Piotr Swat / Shutterstock.com

Founded over three decades ago, semiconductor giant Nvidia (NASDAQ:NVDA) is certainly a company many investors have focused on for a variety of reasons. This chip juggernaut has seen previous surges tied to growth in gaming, crypto and a range of other technological advancements. Computing power demand has risen over time in a relatively exponential fashion, with different driers each time.

Thus, investors shouldnt be surprised to see the company pop on a surge in interest around AI. This catalyst is as real as many of the companys previous catalysts, but many think theres a much longer runway to this particular technology (and for good reason).

On Tuesday, June 18, Nvidia replaced Microsoft (NASDAQ:MSFT) as the worlds most valuable company. Shares rose after the news broke out, rising 3.6%. Currently, Nvidia has a market cap of $2.9 trillion, surpassing both Microsoft and Apple (NASDAQ:AAPL).

Over the past year, NVDA stock has seen a 178% increase due to its successful Q1 earnings report last May. Impressively, this stock has also seen a nine-fold increase since 2022, and its most recent rally can be almost entirely tied to the rise of generative AI. To add more positive news, Nvidias 10-for-1 stock split improved its chances of joining the Dow soon.

Source: T. Schneider / Shutterstock.com

Super Micro Computer (NASDAQ:SMCI) shares rose 10% on Thursday, driven by strong Broadcom (NASDAQ:AVGO) earnings, positive Oracle (NYSE:ORCL) news and AI stock momentum. With surging AI demand driving demand for server hardware and solutions, the server specialists stock has surged nearly 200% this year.

Super Micro Computers rack-scale systems, integrating power, storage, cooling and software, support high-performance Nvidia and AMD (NASDAQ:AMD) AI chips. This demand drove its sales to $3.9 billion in the last fiscal quarter, a 200% increase. Earnings per share surged 308% to $6.65, benefiting from the growing need for complex data processing.

Moreover, the company expanded its manufacturing capabilities globally, including in San Jose, Taiwan and Malaysia. They aimed to increase monthly rack production to 5,000, up from 4,000 last year and 3,000 in 2022. Now, with a strong focus on AI data centers and its 5S Strategy, Supermicro forecasts $25 billion in sales over the next few years, contradicting its 2024 forecast of $14.7 billion.

Source: rafapress / Shutterstock.com

Another AI stock investors may want to consider is Palantir Technologies (NYSE:PLTR), founded in 2003 by Peter Thiel and Alex Karp. While the company has existed for years, it only went public and became a listing in 2020. However, since then, the stock has surged 138%. The companys momentum accelerated from early 2023 with the launch of its Artificial Intelligence Platform (AIP), integrated into platforms like Foundry and Gotham.

PLTR stock has been on the rise, surging during June 20s premarket session. That was tied to news that Palantir secured an exclusive deal to supply data management solutions for the Starlab commercial space station, led by Voyager Space, Airbus SE (OTCMKTS:EADSY), Mitsubishi (OTCMKTS:MSBHF) and MDA Space. CEO Alexander Karp expressed excitement about enhancing global intelligence capabilities on Earth and in space.

Starlab Space and Palantir utilized digital twins and AI to optimize operations. Palantir also secured a $19 million, two-year contract from ARPA-H for critical data infrastructure. Assuming more deals come down the pike, this is an AI stock with some pretty clear catalysts investors are right to focus on right now.

On the date of publication, Chris MacDonald did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.

Chris MacDonalds love for investing led him to pursue an MBA in Finance and take on a number of management roles in corporate finance and venture capital over the past 15 years. His experience as a financial analyst in the past, coupled with his fervor for finding undervalued growth opportunities, contribute to his conservative, long-term investing perspective.

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Hey, Artificial Intelligence Fans! 3 Long-Term AI Stocks to Load Up on Now. - InvestorPlace

What ifApples voice assistant Siri was really, really, really good?

That question is at the heart of much of the tech giants artificial intelligence (AI) research, according to a report Sunday (May 5) by The Verge reviewing those efforts.

For example, a team of Apple researchers has been trying to develop a way to use Siri without having to use a wake word.

Rather than waiting for the user to say Hey Siri or Siri, the voice assistant would be able to intuit whether someone was speaking to it.

This problem is significantly more challenging than voice trigger detection, the researchers did acknowledge per the report, since there might not be a leading trigger phrase that marks the beginning of a voice command.

The Verge report added that this could be why another research team came up with a system to more accurately detect wake words. Another paper trained a model with better understanding of rare words, which are in many cases not well understood by assistants.

Apple is also working on ways to make sure Siri understands what it hears. For example, the report said, the company developed a system called STEER (Semantic Turn Extension-Expansion Recognition) that is designed improve users back-and-forth communication with an AI assistant by trying to determine when the user is asking a follow-up question and when they are asking a new one.

The report comes at a time when Apple appears to be taking as PYMNTS wrote last week a measured approachto its AI efforts.

Among its projects is the ReALM (Reference Resolution As Language Modeling) system, which simplifies the complex process of understanding screen-based visual references into a language modeling task using large language models.

On the one hand, if we havebetter, faster customer experience, theres a lot of chatbots that just make customers angry, AI researcher Dan Faggella, who is not affiliated with Apple, said in an interview with PYMNTS. But if in the future, we have AI systems that can helpfully and politely tackle the questions that are really quick and simple to tackle and can improve customer experience, it is quite likely to translate to loyalty and sales.

The voice tech sector is on the rise. According to research by PYMNTS Intelligence, theres a notable interest amongconsumers in this technology, with more than half (54%) saying they look forward to using it more in the future due to its rapidity.

For all PYMNTS AI coverage, subscribe to the dailyAINewsletter.

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Apple AI Could Produce 'Really Really Good' Version of Siri - PYMNTS.com

Chipmaker Nvidia surpassed Microsoft for the first time this month to become the worlds most valuable company, with a market capitalization of $3.3 trillion. Though its reign on the top of the charts was brief, it crowned a rapid climb for the company, which was little known outside tech circles just two years ago.

For most of its three decades of existence, Nvidia was mostly a niche player, making computer chips for video games, but the companys central position in the artificial intelligence boom has led to a spectacular rise.

Nvidia sells the graphics processing units (GPUs) and the software crucial to training and running the AI algorithms that power chatbots and image generators.

Heres how Nvidia became one of the worlds most valuable companies.

Nvidia went public in January 1999 at $12 a share, six years after its founding and a year before the dot-com crash would wipe out much of the stock market value of the burgeoning internet industry. The company was building a reputation for making some of the best chips for video games, and in 2001, it won a contract to supply GPUs for Microsofts Xbox gaming console.

Nvidia had long been traded by professional investment firms, but during the pandemic, millions of people with day jobs got into stock investing through apps such as Robinhood and online forums like Wall Street Bets. Gamers turned retail investors recognized Nvidia as the company that helped power the improvement in video game graphics over the past two decades.

In 2021, Facebook rebranded itself as Meta and brought renewed interest in the concept of the metaverse a future where people spend much of their time plugged into a virtual world. Nvidia chief executive Jensen Huang jumped on the idea and said his companys chips would power the future world of the metaverse. He even used a digital clone of himself speaking at Nvidias annual conference to showcase the tech.

Metas grand plans for the metaverse have yet to pan out, but at the time, some investors were betting it was the next big thing. On Nov. 4, 2021, financial analysts from Wells Fargo published a report detailing how Nvidia was well positioned to benefit from the prophesied metaverse boom, and the stock jumped 12 percent.

At the end of 2022, OpenAI, an artificial intelligence lab founded as a nonprofit in 2015, unveiled ChatGPT. It was more capable than any chatbot that regular people had interacted with yet. The tech industry was enthralled, and within months, Microsoft had invested billions into OpenAI. The AI arms race was on.

Nvidias chips and software are crucial to building the large language models that serve as the underlying technology in ChatGPT and image generators like OpenAIs Dall-E 3, which launched in 2023.

Huang told investors on Feb. 22, 2023, that the company stood to benefit from the AI boom, which was quickly gaining steam. Wall Street was convinced and the stock shot up 14 percent to give the company a total value of $582.3 billion.

Nvidias stock kept climbing. In May 2023, Nvidia reported earnings showing for the first time with real numbers that it was a prime beneficiary of the AI frenzy. The stock jumped 25 percent and the companys valuation briefly crossed $1 trillion, one of only a handful of companies to ever reach that mark.

As the company reported higher revenue numbers, more investors piled in, pushing the stock up until it ended the year worth $1.2 trillion. Because many AI start-ups and companies, including OpenAI, are not public, there were few options for regular people to invest in the AI boom. Many bought Nvidia stock.

In the first quarter of 2024, Nvidias revenue rose to $26 billion from only $7.2 billion in the same period a year before.

AI start-ups, companies trying to add AI to their products and venture capital firms are all trying to get their hands on Nvidias chips, driving up their price. But the biggest buyers are Big Tech companies Microsoft, Amazon, Meta and Google that need the chips to build and train their own AI models.

Earlier this year, Microsoft, Meta and Google told their investors they would increase spending on AI investments. Google alone plans to spend at least $12 billion every four months this year. Much of that money is going straight into Nvidias coffers.

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See how Nvidia became one of the world's most valuable companies - The Washington Post

Berkshire HathawaysWarren Buffett has compared the development of artificial intelligence (AI) to the atomic bomb.

Just like that invention, the multibillionaire said Saturday (May 4) at Berkshires annual meeting, AI could producedisastrous resultsfor civilization.

We let a genie out of the bottle when we developed nuclear weapons, said Buffett, whose comments were reported by The Wall Street Journal (WSJ). AI is somewhat similar its part way out of the bottle.

While Buffett acknowledged his understanding of AI was limited, he argued he still had cause for concern, discussing a recent sighting of a deepfake of his voice and image. This leads him to believe AI will allow scammers to more effectively pull off their crimes.

If I was interested ininvesting in scamming, its going to be the growth industry of all time, he said.

The WSJ report noted that Buffetts comments come amid a debate among business leaders about how AI will impact society. And while not everyone compares the technology to the atomic bomb, there are those who worry AI will wipe out white-collar jobs.

Others see the upside to AI, like JPMorgan Chase CEO Jamie Dimon has said AI could invent cures for canceror allow more people in future generations to live to 100 years old.

It will create jobs. It will eliminate some jobs. It will make everyone more productive, Dimon said in a recent WSJ interview.

It is also transforming how companies train and upskill their employees, PYMNTS wrote last week, providing personalized learning experiences that can cut costs and improve efficiency.

The global AI-in-education market is projected to expand from $3.6 billion in 2023 to around $73.7 billion by 2033, according to a report from Market.US. But in spite of this impressive forecast, online education companyChegg, which has invested in AI tools, recently saw a decline in stock, something that underscores the sectors volatility.

Generative AI can provide alevel of personalizationin learning that is nearly impossible to achieve without this advanced technology, Ryan Lufkin, global vice president of strategy at the education technology company Instructure, told PYMNTS.

This means we can quickly assess what an employee knows and teach directly to their knowledge gaps, reducing the amount of time spent learning and improving time-to-productivity.

For all PYMNTS AI coverage, subscribe to the dailyAINewsletter.

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Warren Buffett Warns of AI Use in Scams - PYMNTS.com

Today, the U.S. Department of Health and Human Services (HHS) publicly shared its plan for promoting responsible use of artificial intelligence (AI) in automated and algorithmic systems by state, local, tribal, and territorial governments in the administration of public benefits. Recent advances in the availability of powerful artificial intelligence (AI) in automated or algorithmic systems open up significant opportunities to enhance public benefits program administration to better meet the needs of recipients and to improve the efficiency and effectiveness of those programs.

HHS, in alignment with OMB Memorandum M-24-10, is committed to strengthening governance, advancing responsible innovation, and managing risks in the use of AI-enabled automated or algorithmic systems. The plan provides more detail about how the rights-impacting and/or safety-impacting risk framework established in OMB Memorandum M-24-10 applies to public benefits delivery, provides information about existing guidance that applies to AI-enabled systems, and lays out topics that HHS is considering providing future guidance on.

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HHS shares its Plan for Promoting Responsible Use of Artificial Intelligence in Automated and Algorithmic Systems by ... - HHS.gov