Category Archives: Ai

For enterprise cybersecurity initiatives to be effective today, they must be continuous and proactive. Organizations simply cant risk a real breach to test their security mettle. But what does it take for cybersecurity strategies to be deemed proactive? Usually, it implies a balanced mix of observability and continuous verification.

Penetration testing has emerged as one way to continuously test the fidelity of networking and data infrastructures by mirroring an actual malicious attack. Horizon3 AI Inc. offers pentesting as a service through its NodeZero platform. NodeZeros growing popularity and appeal across a global user base, in addition to Horizon3s channel-based go-to-market strategy was the focus of a recent livestream event.

Industry analyst John Furrier, host of theCUBE, SilicionANGLE Medias livestreaming studio, hosted the Horizon3.ai Drives Global Partner-First Approach With Expansion of PartnerProgram event. In three separate interviews, Furrier spoke with Horizon3sRainer M. Richter, vice president of EMEA and APAC;Chris Hill, sector head for strategic accounts/federal; andJennifer Lee, head of channel sales, Americas lead. They discussed enterprise use cases and topics on how organizations can maintain agile cybersecurity structures.(* Disclosure below.)

Here are three insights you might have missed:

Data is the enterprises currency, and often its the target or conduit of a malicious attack. With companies constantly ingesting and processing unprecedented swathes of data, such an entry point must be a security priority.This call for better care extends to solutions providers especially, as they are often the direct custodians of multiple customers data. The Horizon3/Splunk partnership perfectly exemplifies this concept, according to Hill.

What weve been able to do with Splunkis build a purpose-built solution that allows Splunkto eat more data, Hill said. So, Splunk itselfis an ingest engine, and the great reason people buy it is to buildthese really fast dashboardsand grab intelligence out of it. With NodeZero,sure we do pentesting,but because were an autonomous pentesting tool,we do it continuously.

In platform partnerships, results are the preeminent measure of value. And, yet again, the Splunk example is handy for determining NodeZeros true enterprise value. Alongside enabling multi-tier users to glean their exposed areas, it has also created visibility to high-impact data logs and enabled asset discovery, according to Hill.

One of the cool things that we can dois actually create this low-code, no-code environment.So Splunk customers, for instance, can use Splunk SOAR to actually triageevents and prioritize that event, he said.

Heres Chris Hills complete video session:

Horizon3 has carved a niche that caters to managed service providers, managed security service providers and consultancy partner ecosystems. That spectrum is much wider, however, as the company is also entrenching itself with resale, systems integrators, technology and cloud partners.

Then weve got our cloud partners.We are in Amazon Web Services Marketplace andwere part of the ISV Accelerate Program, Lee said.So were doing a lot there with our cloud partners.And, of course, we go to marketwith distribution partners as well.

Horizons NodeZero continuous autonomous penetration testing platform offers a certification program, including separate seller and operator portions both of which are offered virtually and at no extra cost to partners, according to Lee.

Its live virtually but not self-paced.And we also have in-person sessions as well.We also can customize these to any partnersthat have a large group of people.And we can do one in-personor virtual just specifically for that partner, Lee added.

Heres Jennifer Lees complete video session:

Horizon3 serves a diverse range of partner sizes, but it appears the smaller-sized early adapters account for a considerable share of the buzz around NodeZero, according to Richter.

They immediately understand where the value isand that they can change their offering, he explained. Theyre changing their offeringin terms of penetration testingbecause they can do more pentestsand they can then add other ones.

From previously having to source pentesting expertsto get the pentest at a particular customer done, they can now do that independently with NodeZero, according to Richter.More importantly, NodeZero isnt thought of as a replacement for the traditional pentesters job, but rather as a tool with which to do pentestings foundational work.

We are providing with NodeZerosomething like the foundational workof having an ongoing penetration testingof the infrastructure and operating system.And the pentesters by themselvescan concentrate in the futureon things like application pentesting, for example.So we are not killing the pentest, Richter stated.

Heres Rainer M. Richters complete video session:

You can also watch the entireHorizon3.ai Drives Global Partner-First Approach event on-demand below, or visittheCUBEs exclusive event website:

(* Disclosure: TheCUBE is a paid media partner for the Horizon3.ai Drives Global Partner-First Approach livestream event. Neither Horizon3 AI, the sponsor of theCUBEs event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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Three things you might have missed from the 'Horizon3.ai Drives Global Partner-First Approach' event - SiliconANGLE News

PALO ALTO, Calif.--(BUSINESS WIRE)--Uniphore, the leader in Conversational Automation, today announced that Everest Group, a leading strategic research and analyst firm, has recognized its conversational AI and automation platform as a Major Contender in its 2022 Conversational AI Technology Vendor Landscape with Products PEAK Matrix Assessment.

Uniphores flagship conversational AI and automation platform is the industrys only platform that delivers a complete analysis of intent, sentiment, emotion and tone for every contact center conversation. With these robust capabilities, enterprises can transform the complete customer and agent experience. Uniphores platform combines AI, Machine Learning, RPA, Natural Language Processing (NLP), Knowledge AI and more to drive new experiences and maximize the efficiency and cost savings for its customers.

Were honored to be recognized by Everest Group as a Major Contender in the Conversational AI industry. At Uniphore, weve always been focused on the importance and the value of conversations in their entirety, and that shines through in this report, said Umesh Sachdev, CEO and Co-founder at Uniphore. Our platform approach answers the natural evolution of the Conversational AI market, which has for a long time focused solely on self-service offerings opposed to the full spectrum that benefits both customer and agent. We look forward to continuing to lead the way in the market with our unique focus on the critical conversations of todays modern enterprises.

Everest Groups PEAK Matrix Assessment evaluated 26 global Conversational AI vendors on their market impact, as well as their vision and capability to deliver services.

In the report, Uniphores many strengths are highlighted, including:

Uniphore is positioned as a Major Contender in Everest Group's Conversational AI PEAK Matrix. Uniphore offers a detailed conversational AI solution that utilizes advanced capabilities such as sentiment analysis, knowledge AI, RPA integrations, and a low-code/no-code platform, said Sharang Sharma, Practice Director, Everest Group. Uniphores built-in reports and dashboards analyze conversational data to help with strategic planning. It drives real-time contextualized engagement with customers to improve closure rates and operational efficiency. The technology vendors recent acquisitions to strengthen capabilities in its existing native-voice channel and other areas for improved CX will be critical in securing its position in the existing markets, while expanding in fast-growing regions such as APAC and LATAM.

A complimentary copy of the report is available for download here.

About Uniphore

Uniphore is the global leader in Conversational Automation. Every day, billions of conversations take place across industries customer service, sales, HR, education and more. Whether they are human to human, human to machine or machine to machine, conversations are at the heart of everything we do, and the new currency of the enterprise.

At Uniphore, we believe companies that best understand and take action on those conversations will win. We have built the most comprehensive and powerful platform that combines conversational AI, computer vision, emotion and tonal analysis, workflow automation, and RPA (Robotic Process Automation) with a business-user-friendly UX in a single integrated platform to transform and democratize customer experiences across industries.

Follow our blog and connect with us on LinkedIn, Twitter, Facebook, and Instagram.

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Uniphore Recognized as Major Contender in Conversational AI by Latest Everest Group PEAK Matrix - Business Wire

MELVILLE, N.Y.--(BUSINESS WIRE)--Verint (Nasdaq: VRNT), The Customer Engagement Company, today announced it was named to the inaugural Constellation ShortList for Conversational AI. The technology vendors and service providers included in this research deliver critical transformation initiative requirements for early adopters and fast-follower organizations.

Supported by a natural language understanding library of over 90,000 intents, Verint Conversational AI goes beyond simple question-and-answer interactions to provide actionable responses across channels including voice, social media channels, and smart speakers. These capabilities are the foundation for Verint Intelligent Virtual Assistant (IVA). Verint IVA can answer questions 24/7 in more than 40 languages, proactively assist customers, provide guided resolution, capture insights, and transfer interactions to live agents.

Today, brands need to provide swift and effortless customer experience on their customers channel of choice. To remain competitive, organizations must put digital-first engagement at the top of their priority lists, says Verints Heather Richards, vice president, GTM strategy, digital first engagement. Through its conversational AI capabilities, the Verint IVA solution delivers personalized, human-like interactions with customers across digital and voice channels.

Constellation considers a number of criteria when choosing solutions for their shortlist. Conversational AI solutions must integrate natural-language-understanding (NLU) capabilities, understand users and personalize conversations for each user, enable a live agent escalation option if and when needed, and provide customizable workflow management, to name a few.

Conversational AI (CAI) has moved away from traditional chatbots to intelligent virtual agents, often matching, or surpassing, the human agents. In many instances, humans can now have intelligent conversations with machines without realizing they are talking to a machine, said Andy Thurai, vice president and principal analyst at Constellation Research. Today's CAI systems are purpose-built for a specific domain and can solve customer problems without the need for human intervention. The combination of sentiment, tone, and emotional intelligence allows them to determine if a customer is upset and prioritizes solving their issue which helps reduce agitation."

Constellation Research advises leaders on leveraging disruptive technologies to achieve business model transformation and streamline business processes. Products and services named to the Constellation ShortList meet the threshold criteria for this category as determined through client inquiries, partner conversations, customer references, vendor selection projects, market share, and internal research. The portfolio is updated at least once per year as the analyst team deems necessary based on market conditions.

Visit Verint Conversational AI to learn more.

Disclaimer: Constellation Research does not endorse any solution or service named in its research.

About Verint

Verint (Nasdaq: VRNT) helps the worlds most iconic brands including over 85 of the Fortune 100 companies build enduring customer relationships by connecting work, data and experiences across the enterprise. The Verint Customer Engagement portfolio draws on the latest advancements in AI and analytics, an open cloud architecture, and The Science of Customer Engagement to help customers close The Engagement Capacity Gap.

Verint. The Customer Engagement Company. Learn more at Verint.com.

This press release contains forward-looking statements, including statements regarding expectations, predictions, views, opportunities, plans, strategies, beliefs, and statements of similar effect relating to Verint Systems Inc. These forward-looking statements are not guarantees of future performance and they are based on management's expectations that involve a number of risks, uncertainties and assumptions, any of which could cause actual results to differ materially from those expressed in or implied by the forward-looking statements. For a detailed discussion of these risk factors, see our Annual Report on Form 10-K for the fiscal year ended January 31, 2022, and other filings we make with the SEC. The forward-looking statements contained in this press release are made as of the date of this press release and, except as required by law, Verint assumes no obligation to update or revise them or to provide reasons why actual results may differ.

VERINT, VERINT DA VINCI, THE CUSTOMER ENGAGEMENT COMPANY, BOUNDLESS CUSTOMER ENGAGEMENT, THE ENGAGEMENT CAPACITY GAP and THE SCIENCE OF CUSTOMER ENGAGEMENT are trademarks of Verint Systems Inc. or its subsidiaries. Verint and other parties may also have trademark rights in other terms used herein.

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Verint Named to Constellation ShortList for Conversational AI - Business Wire

Microcontrollers, miniature computers that can run simple commands, are the basis for billions of connected devices, from internet-of-things (IoT) devices to sensors in automobiles. But cheap, low-power microcontrollers have extremely limited memory and no operating system, making it challenging to train artificial intelligence models on edge devices that work independently from central computing resources.

Training a machine-learning model on an intelligent edge device allows it to adapt to new data and make better predictions. For instance, training a model on a smart keyboard could enable the keyboard to continually learn from the users writing. However, the training process requires so much memory that it is typically done using powerful computers at a data center, before the model is deployed on a device. This is more costly and raises privacy issues since user data must be sent to a central server.

To address this problem, researchers at MIT and the MIT-IBM Watson AI Lab developed a new technique that enables on-device training using less than a quarter of a megabyte of memory. Other training solutions designed for connected devices can use more than 500 megabytes of memory, greatly exceeding the 256-kilobyte capacity of most microcontrollers (there are 1,024 kilobytes in one megabyte).

The intelligent algorithms and framework the researchers developed reduce the amount of computation required to train a model, which makes the process faster and more memory efficient. Their technique can be used to train a machine-learning model on a microcontroller in a matter of minutes.

This technique also preserves privacy by keeping data on the device, which could be especially beneficial when data are sensitive, such as in medical applications. It also could enable customization of a model based on the needs of users. Moreover, the framework preserves or improves the accuracy of the model when compared to other training approaches.

Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a broader reach, especially for low-power edge devices, says Song Han, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the MIT-IBM Watson AI Lab, and senior author of the paper describing this innovation.

Joining Han on the paper are co-lead authors and EECS PhD students Ji Lin and Ligeng Zhu, as well as MIT postdocs Wei-Ming Chen and Wei-Chen Wang, and Chuang Gan, a principal research staff member at the MIT-IBM Watson AI Lab. The research will be presented at the Conference on Neural Information Processing Systems.

Han and his team previously addressed the memory and computational bottlenecks that exist when trying to run machine-learning models on tiny edge devices, as part of their TinyML initiative.

Lightweight training

A common type of machine-learning model is known as a neural network. Loosely based on the human brain, these models contain layers of interconnected nodes, or neurons, that process data to complete a task, such as recognizing people in photos. The model must be trained first, which involves showing it millions of examples so it can learn the task. As it learns, the model increases or decreases the strength of the connections between neurons, which are known as weights.

The model may undergo hundreds of updates as it learns, and the intermediate activations must be stored during each round. In a neural network, activation is the middle layers intermediate results. Because there may be millions of weights and activations, training a model requires much more memory than running a pre-trained model, Han explains.

Han and his collaborators employed two algorithmic solutions to make the training process more efficient and less memory-intensive. The first, known as sparse update, uses an algorithm that identifies the most important weights to update at each round of training. The algorithm starts freezing the weights one at a time until it sees the accuracy dip to a set threshold, then it stops. The remaining weights are updated, while the activations corresponding to the frozen weights dont need to be stored in memory.

Updating the whole model is very expensive because there are a lot of activations, so people tend to update only the last layer, but as you can imagine, this hurts the accuracy. For our method, we selectively update those important weights and make sure the accuracy is fully preserved, Han says.

Their second solution involves quantized training and simplifying the weights, which are typically 32 bits. An algorithm rounds the weights so they are only eight bits, through a process known as quantization, which cuts the amount of memory for both training and inference. Inference is the process of applying a model to a dataset and generating a prediction. Then the algorithm applies a technique called quantization-aware scaling (QAS), which acts like a multiplier to adjust the ratio between weight and gradient, to avoid any drop in accuracy that may come from quantized training.

The researchers developed a system, called a tiny training engine, that can run these algorithmic innovations on a simple microcontroller that lacks an operating system. This system changes the order of steps in the training process so more work is completed in the compilation stage, before the model is deployed on the edge device.

We push a lot of the computation, such as auto-differentiation and graph optimization, to compile time. We also aggressively prune the redundant operators to support sparse updates. Once at runtime, we have much less workload to do on the device, Han explains.

A successful speedup

Their optimization only required 157 kilobytes of memory to train a machine-learning model on a microcontroller, whereas other techniques designed for lightweight training would still need between 300 and 600 megabytes.

They tested their framework by training a computer vision model to detect people in images. After only 10 minutes of training, it learned to complete the task successfully. Their method was able to train a model more than 20 times faster than other approaches.

Now that they have demonstrated the success of these techniques for computer vision models, the researchers want to apply them to language models and different types of data, such as time-series data. At the same time, they want to use what theyve learned to shrink the size of larger models without sacrificing accuracy, which could help reduce the carbon footprint of training large-scale machine-learning models.

AI model adaptation/training on a device, especially on embedded controllers, is an open challenge. This research from MIT has not only successfully demonstrated the capabilities, but also opened up new possibilities for privacy-preserving device personalization in real-time, says Nilesh Jain, a principal engineer at Intel who was not involved with this work. Innovations in the publication have broader applicability and will ignite new systems-algorithm co-design research.

On-device learning is the next major advance we are working toward for the connected intelligent edge. Professor Song Hans group has shown great progress in demonstrating the effectiveness of edge devices for training, adds Jilei Hou, vice president and head of AI research at Qualcomm. Qualcomm has awarded his team an Innovation Fellowship for further innovation and advancement in this area.

This work is funded by the National Science Foundation, the MIT-IBM Watson AI Lab, the MIT AI Hardware Program, Amazon, Intel, Qualcomm, Ford Motor Company, and Google.

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Learning on the edge | MIT News | Massachusetts Institute of Technology - MIT News

Artificial intelligence is at the heart of pharmas digital transformation. When deployed correctly, AI algorithms reduce manual labor, speed access to insights, and provides quality teams with more robust information in less time to support more effective decision-making.

But it only works if the AI is proven reliable, and users trust the output.

If you're using AI in a critical system it has to be reliable, and it has to be designed in a way that the worst-case scenario is not going to lead to failure, said Matt O'Donnell, Global Lead, Life Sciences ISV Partners at Microsoft.

O'Donnell recently participated in the IQVIA webinar: Futureproofing Your Quality Operations Through Digital Transformation, where he and Mike King, Senior Director, Product & Strategy at IQVIA discussed the evolution of AI in pharma, and how it is driving innovation in the quality environment.

For most pharma leaders, this transformation has only just begun. While the majority of webinar attendees believe that AI in quality operations will improve process time and consistency of performance (54%), 72% admitted that they arent yet using AI in their quality operations.

That set the tone for the conversation, where ODonnell and King discussed how AI is being used in pharma today, where it can add value, and how companies can access all of the benefits of AI in a safe and compliant way.

Below are some of the insights we captured from their conversation.

ODonnell: AI simulates human decision-making and imitates human intelligence to deliver stronger, more accurate, and more repeatable solutions. It can copy all of the cognitive senses, and combine deep knowledge and search capabilities to identify connections that might not have been known before.

King: AI can also help capture insights from structured data in a more timely manner. For example, many companies have a record of submission pathways for audits of nonconformances, or of CAPA (Corrective and Preventive Actions) closures, which results in a huge volume of data. Using AI, they can mine precedents in that data to understand what decisions were taken historically, what potential pathways lead to certain outcomes, and where additional pathways or alternatives could bring faster, safer decision making. That's where AI can really provide insights across the quality management systems that we operate in.

ODonnell: Consider vision. So much information in healthcare is captured in a semi-structured way. It might be in a PDF, or a patient scan, or a handwritten record. That's valuable information but to unlock it, you first need to use Optical Character Recognition to scan it and turn it into a structured document so the system can process the data.

King: The key is combining the technology with the knowledge of our teams to make better decisions that are more predictable and more consistent. There are great opportunities to use targeted algorithms to identify patterns and trends that may not be seen by the human eye.

King: Connected intelligence is the bringing together of systems driven by intelligence, with the support of AI to rapidly focus on those insights that we may not yet understand. AI enables connected intelligence so that we can capture relevant insights in a timely manner to drive the right actions.

ODonnell: That speed is essential, especially for quality surveillance of a drug or medical device in the market. Monitoring for early indications of adverse events requires reviewing thousands of documents to find connections that make sense. AI can find those connections, making it easy for quality teams to gain knowledge from semi structured and unstructured data.

ODonnell: We are often asked whether AI is appropriate for healthcare, and whether it is sufficient. I always point to the progress weve made. Over the last six years, AI systems have reached near human parity for general purpose. And while the medical domain is more challenging, the next wave of transformation in this space is already happening, bringing AI at scale.

We already see great accuracy with natural language processing, which when it's trained on medical publications can deliver 90+ percent accuracy. And when an algorithm is trained for a highly specific medical task, like identifying and tracking brain tumors, it can be extremely accurate.

We continue to make improvements, by using the trillions of healthcare documents we have access to train algorithms and create better simulations of human intelligence.

King: Its also important that stakeholders be able to trust the technology. For an organization to adopt AI, it must quantify the benefits in terms of consistency, resource utilization, and in identifying things that the human eye and the human brain cannot find on its own. Only when organizations quantify that benefit and present it to their various stakeholders is there an opportunity for true adoption.

King: Many companies take a staged approach. Some start with the safety field, using AI to manage adverse event reports and to process product complaints; where others may start by embedding AI in a quality management system to support audits, inspections, and document drafting. It depends on where the opportunity lies for the organization, where their strengths lie, and where those discussions land with senior stakeholders.

ODonnell: There is a shift going on right now. It's not just about one company producing great AI models. I see collaborations between academia, different companies, and different technology providers. They are coming together to produce the best AI models that can advance benefits for the entire human population.

King: There are so many applications for AI, and so many opportunities to use sophisticated algorithms to help organizations enhance the focus on patient safety by being able to detect signals that we may not otherwise see. Once companies weigh the benefits, the potential risks and the costs they will see the incredible value of AI and how it can enhance activities across the operation.

To hear more of their conversation, click here to listen to the entire webinar, or contact Regulatory_Quality_Compliance@iqvia.com to learn more.

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AI will Transform your Quality Operations - IQVIA

As artificial intelligence becomes more advanced, AI image and writing generators are becoming more widespread, even taking on creative tasks some once thought uniquely human.

These tools have limitations. AI-created images sometimes appear half-finished (look no further than DALL-Es early renderings of faces), and AI-generated writing can sound like garble written by, well, a robot.

The surge in AI use for creative work like copywriting and developing art has some in the creative fields concerned about losing their jobs, going the way of the traditional animator at Pixar. Reports like one published in 2021 by San Mateo-based job discovery platform Zippia dont help with statements like, AI could take the jobs of as many as one billion people globally and make 375 million jobs obsolete over the next decade and half of all companies currently utilize AI in some fashion.

Using AI to create open-source art available to the masses wasnt on the radar for many until the release of the text-to-image creator DALL-E Mini last summer. The release coincided with the Washington Posts profile of Google engineer Blake Lemoine, who claimed Googles Language Model for Dialogue Applications (LAMDA) was sentient.

AI innovations like GPT-3a large language model which uses deep learning to produce original textare touted as solutions to a host of problems with little discussion about drawbacks or limitations. One notable example is the widely-used writing assistant Grammarly, which uses a combination of artificial intelligence techniques, including deep learning and natural language processing.

Hour Ones Natalie Monbiot says creatives shouldnt be concerned about AI.

It's normal to feel anxious about it, and it will be a realistic concern for those whose actual work can be done more cheaply, quickly, and consistently via machines, says Monbiot, who is head of strategy for the avatar video generation platform.

These new technologies are new tools, she says, like the pen, the typewriter, computers, and so on.

Monbiot says that as AI becomes more instrumental to creators work, there will be a higher premium on creativity (which is distinctly human) and less on execution.

Kris Ruby of Ruby Media Group, a PR agency, tells dot.LA that users go wrong with AI writing products by trusting them to produce finished work. That is not how the tools are supposed to be used, Ruby says.

According to Ruby, users of text-to-image generation tools like DALL-E Mini and Midjourney make the mistake of calculating the cost of the software subscriptionbut not the number of hours it takes to get even one useable image.

Austin-based Jasper.ais CEO Dave Rogenmoser says these applications eliminate the mundane elements of the content creation process. Jasper develops multiple AI-powered writing tools and recently added a text-to-image creator to its suite.

It isnt a replacement for creators or the creative process, he says, rather, its a trusty sidekick in the content process that helps bring ideas to life faster and in a more efficient way.

San Francisco-based Writer.com is an AI writing assistant focused on corporate clients. Its CEO, May Habib, tells dot.LA that creators have more to gain from the tools than they have to lose.

Like any tool, it is about depth: AI writing tools are most powerful in the hands of those who are already pretty skilled, but still pretty useful for everyone, Habib says.

We dont think AI is going to take away real writing jobs, she continues, but it will speed up ideation and drafting.

Is there a danger of overselling AI before it can meet companies expectations?

Habibs answer? Absolutely. Consumers should not expect artificial intelligence to solve all their problems. Applications powered by AI cant feel like magic, she says; they have to feel like technology."

AI expert Mikaela Pisani is the Chief Data Scientist for Los Angeles-based Rootstrap, which develops apps for startups. Asked if it was realistic for creators to worry about losing jobs to artificial intelligence, Pisani says, AI is becoming increasingly creative and can help creatives generate content ideas at scale.

When it comes to fears that AI might replace creators, Pisani notes that Creativity is defined as 'the ability to produce or use original and unusual ideas.

To think outside of the box is implicitly hard to do for machines, Pisani says, since AI are trained on available information. Therefore, our creative brain won't be replaced by AI in the near future, since it is too challenging for machines to recreate innovation. By extension, AI does not create a final piece of art, but it can be used as a co-creator.

Pisanis perspective isnt that different from execs behind AI-fueled startups. She says that because artificial intelligence can multitask rapidly, it could also be a source of inspiration for artists.

Writers, musicians, designers, or artists, Pisani continues, shouldn't be afraid of being replaced but should make themselves aware of these AI tools that can help their creativity reach a new level of scale."

So far, the consensus seems to be that AI is just an instrument, not a replacement for human artistry.

Its still early, though, and artificial intelligence use is evolving fast. Just last week, Vanity Fair reported that 91-year-old James Earl Jones is retiring from voicing Darth Vader for future Star Wars shows and movies. His replacement? Respeecher, AKA voice cloning powered by artificial intelligence. The Ukraine-based company says its product leverages recent revolutionary advances in artificial intelligence to create voice swaps [that] are virtually indistinguishable from the original and never sound robotic.

One thing seems clear: AI is here to stay.

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Is AI Making the Creative Class Obsolete? - dot.LA

Companies are increasingly turning to artificial intelligence (AI) to automate and optimize business functions. But according to recent research, the IT professionals who will be asked to implement the technology have decidedly mixed feelings about it, ranging from optimism to outright dread (and sometimes both at the same time).

That's according to the 2023 State of IT report(Opens in a new window) from PCMag's sister site Spiceworks Ziff Davis(Opens in a new window) (SWZD). For its research, the company asked 968 IT buyers from businesses in North America and Europe whether their organizations currently used AI or planned to do so. Among those who answered affirmatively, answers to follow-up questions were revealing.

On the positive side, many IT pros see AI as a beneficial technology that can help advance their careers. Fully 74% of survey respondents agreed with the statement, "AI will automate tasks and enable more time to focus on strategic IT initiatives." In other words, they have faith that AI tools will free them from the more mundane chores of their roles and allow them to concentrate on tasks that add value to the business.

Other opinions were more sanguine, with 67% saying "AI will be a mission-critical element of our business strategy in the years to come." (Fair enough.)

(Credit: Spiceworks Ziff Davis)

Still others seemed to be envisioning a science-fiction future that resembles movies more than reality. When asked to respond to the prompt, "I expect to work alongside intelligent robots/machines in the next 5 years," 62% of those surveyed responded yes.

What does it all mean? Clearly, the IT professionals surveyed see AI usage in modern business as an inevitability. As the cost of entry of AI continues to trend downward, business software vendors will increasingly offer AI capabilities as differentiating features.

Then again, the same IT pros surveyed by SWZD saw serious potential downsides to the growth of AI. Just over half of the respondents agreed with the statement, "AI will put IT jobs at risk." As was the case with earlier phases of IT automation, some professionals fear that AI technologies could eventually become so effective that it will put humans out of work.

Even more survey respondents were concerned about how AI will be used for data analysis, particularly when it comes to user data. The prompt, "AI will create major data privacy issues" drew agreement from 55% of respondents.

But some respondents' fears run even deeper. A remarkable 49% agreed with the statement, "Innovation in AI presents an existential threat to humanity"perhaps recalling storylines from dystopian science fiction. They wouldn't be alone; no less than Tesla and SpaceX billionaire Elon Musk famously described AI as "summoning the demon."

Whatever their personal feelings, however, most survey respondents seemed to agree that AI is here to stay, citing applications ranging from data analytics and automation to security intrusion and fraud detection, natural language processing, web and social media analytics, and more.

Editors' Note: Spiceworks is owned by Ziff Davis, the parent company of PCMag.

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Survey: IT Pros Remain Conflicted Over AI's Potential, Peril - PCMag

What do more than 2,000 data scientists, engineers and machine learning professionals have in common? Theyre all getting ready to hear from a fantastic lineup of speakers at the iMerit ML DataOps Summit on November 8.

Over the course of the summit, youll hear from 18 of the the most influential and forward-thinking leaders in AI, data science, engineering and ML. Were highlighting just three today, but be sure to check out all the speakers and learn more about them.

Pro tip: This is a free online event. Register now, mark your calendar and get ready for an exciting deep dive into the ML DataOps landscape.

Ready? Lets shine the data spotlight on three of the sectors leading movers and makers.

Abhijit Bose, Capital One

Abhijit Bose, currently managing VP and head of the center for machine learning at Capital One, has led AI/ML engineering teams at some of the largest tech and financial services firms such as Facebook and JP Morgan. He understands the criteria for building impactful ML platforms and what is top of mind for ML engineering teams today.

As a leading voice in machine learning in the enterprise, Bose is passionate about building world-class organizations and enterprise-wide AI platforms that advance capabilities in personalization, recommendations, ad targeting, marketing sciences and fraud/anomaly detection.

Sriram Subramanian, Microsoft

Subramanian is currently the global lead for Data and AI domain at the FastTrack for Azure group within Microsoft. Before joining Microsoft, he was a research director at IDC covering AI / ML lifecycle management software. Major themes of his research included MLOps, Trustworthy AI, AI Build and Data Labeling software.

Prior to IDC, Subramanian founded and served as principal analyst at CloudDon, an independent market research and advisory services firm, where his research focused on advising vendors and buyers on cloud-native technologies and stacks.

Vinesh Sukumar, Qualcomm Technologies

Dr. Vinesh Sukumar currently serves as senior director of product management at Qualcomm Technologies, Inc. As head of AI/ML, he leads AI product definition, strategy and solution deployment across multiple business units.

Sukumars nearly 20 years of industry experience spans across research, engineering and application deployment. He holds a doctorate degree specializing in imaging and vision systems and an MBA focused on strategy and marketing. A regular speaker at many AI industry forums, Sukumar has authored several journal papers and two technical books.

The iMerit ML DataOps Summit takes place on November 8 and will be presented across two time zones (North America and APAC). Dont miss this opportunity to learn from some of the best minds in AI, data science, engineering and ML. Register for free today!

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Hear game-changing AI and ML leaders at the iMerit ML DataOps Summit - TechCrunch

Artificial Intelligence (AI) remains hard to define. When it comes to a definition of "intelligence", context is vital and it starts with what we want the AI system to do. It is specific to the application. For example, intelligence for a search engine shouldnt be the same as intelligence for an autonomous vehicle.

Now, with AI systems already in widespread production for more than a quarter of enterprises, businesses must ensure that employees are upskilled to effectively define and implement AI systems, and understand how to manage these systems safely in the workplace. But what does that look like in practice?

Invest in engagement, training and upskilling

The range of AI applications is vast, and there will be few that match the power of LaMDA and other such LLMs, for example, GPT-3 or OPT-175B. However, the story of LaMDAs 'human' conversation further highlights that organizations must be mindful of how they engage with AI systems. Such conversations must be had across the workforce before misinformation, fear, or skepticism takes hold. Beyond that, organizations must also invest in greater engagement, training and upskilling around AI -- and this must be holistic.

Over the next five years, we can expect an explosion of specialized bots within the workplace; employees will be exposed to systems that can make decisions and use language in amazing ways. However, not all employees will embrace this new world, the threat of man-to-machine replacement looms large. For those whose roles may significantly change due to the implementation of automation, it will be vital to encourage the development of a growth mindset.

This is where employees are primed for AI up-skilling by presenting the future as a positive challenge and how AI skills will support their future career growth and success. Mindset will be a huge differentiator going forward, and companies that educate employees early and cultivate a positive AI culture will enjoy manifold benefits. This can include decisively identifying positive AI use cases early and clarifying how these implementations will benefit employees, for example, through reduced time on repetitive or mundane tasks.

The time saved on performing admin tasks can instead be used by employees to learn new skills and impact the business in a new, innovative way. For example, AI can take on repetitive, administrative tasks, such as reporting. However, it is then for the organization to enable their employees to replace that work with more engaging and strategic activities. And, when it comes to AI, it will not just be technical training thats required. Employees will also need to develop new skills to help identify new business opportunities harnessing the technology and take an active role in communication around these technologies, their benefits and risks. Either way, training will be integral.

Holistic training

As UKRI (UK Research & Innovation) highlights, "To make a success of data and AI, organizations need to look at the full AI project supply chain. This starts with identifying a business opportunity that can benefit from AI all the way through to the validation, implementation, testing and deployment. Once the product or service has been deployed, organizations must consider longer-term adoption, maintenance, risks, governance."

To realize the benefits of AI, organizations must invest in holistic training across this chain. Leaders must be clear about what AI can and cannot do, what it should and should not do, and invest in the essential role of human oversight and understanding in making AI viable.

This investment includes ensuring learning delivers clear benefits to employees and organizations alike, providing the foundations for future-proof careers built on meaningful work.

What is clear then, is that sentience is not the goal. It is to deliver better outcomes -- for organizations, employees and society alike. That starts with engaging workforces in holistic AI learning now.

Image credit:AlienCat/depositphotos.com

Mike Loukides is VP of Emerging Tech at OReilly.

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AI is nothing without skilled human oversight - BetaNews

Electrons whizzing through a grid-like lattice don't behave at all like pretty silver spheres in a pinball machine. They blur and bend in collective dances, following whims of a wave-like reality that are hard enough to imagine, let alone compute.

And yet scientists have succeeded in doing just that, capturing the motion of electrons moving about a square lattice in simulations that until now had required hundreds of thousands of individual equations to produce.

Using artificial intelligence (AI) to reduce that task down to just four equations, physicists have made their job of studying the emergent properties of complex quantum materials a whole lot more manageable.

In doing so, this computing feat could help tackle one of the most intractable problems of quantum physics, the 'many-electron' problem, which attempts to describe systems containing large numbers of interacting electrons.

It could also advance a truly legendary tool for predicting electron behavior in solid state materials, the Hubbard model all the while bettering our understanding of how handy phases of matter, such as superconductivity, occur.

Superconductivity is a strange phenomenon that arises when a current of electrons flow unimpeded through a material, losing next to no energy as they slip from one point to another. Unfortunately most practical means of creating such a state rely on insanely low temperatures, if not ridiculously high pressures. Harnessing superconductivity closer to room temperature could lead to far more efficient electricity grids and devices.

Since achieving superconductivity under more reasonable conditions remains a lofty goal, physicists have taken to using models to predict how electrons could behave under various circumstances, and therefore which materials make suitable conductors or insulators.

These models have their work cut out for them. Electrons don't roll through the network of atoms like tiny balls, after all, with clearly defined positions and trajectories. Their activity is a mess of probability, influenced not only by their surroundings but by their history of interactions with other electrons they've bumped into on the way.

When electrons interact, their fates can become intimately intertwined, or 'entangled'. Simulating the behavior of one electron means tracking the range of possibilities of all electrons in a model system at once, which makes the computational challenge exponentially harder.

The Hubbard model is a decades-old mathematical model that describes the confusing motion of electrons through a lattice of atoms somewhat accurately. Over the years and much to physicists' delight, the deceptively simple model has been experimentally realized in the behavior of a wide array of complex materials.

With ever-increasing computer power, researchers have developed numerical simulations based on Hubbard model physics that allow them to get a grip on the role of the topology of the underlying lattice.

In 2019, for instance, researchers proved the Hubble Model was capable of representing superconductivity higher-than-ultra-cold temperatures, giving the green light to researchers to use the model for deeper insights into the field.

This new study could be another big leap, greatly simplifying the number of equations required. Researchers developed a machine-learning algorithm to refine a mathematical apparatus called a renormalization group, which physicists use to explore changes in a material system when properties such as temperature are altered.

"It's essentially a machine that has the power to discover hidden patterns," physicist and lead author Domenico Di Sante, of the University of Bologna in Italy, says of the program the team developed.

"We start with this huge object of all these coupled-together differential equations" each representing pairs of entangled electrons "then we're using machine learning to turn it into something so small you can count it on your fingers," Di Sante says of their approach.

The researchers demonstrated that their data-driven algorithm could efficiently learn and recapitulate dynamics of the Hubbard model, using only a handful of equations four to be precise and without sacrificing accuracy.

"When we saw the result, we said, 'Wow, this is more than what we expected.' We were really able to capture the relevant physics," says Di Sante.

Training the machine learning program using data took weeks, but Di Sante and colleagues say it could now be adapted to work on other, tantalizing condensed-matter problems.

The simulations thus far only capture a relatively small number of variables in the lattice network, but the researchers expect their method should be fairly scalable to other systems.

If so, it could in the future be used to probe the suitability of conducting materials for applications that include clean energy generation, or to aid in the design of materials that may one day deliver that elusive room-temperature superconductivity.

The real test, the researchers note, will be how well the approach works on more complex quantum systems such as materials in which electrons interact at long distances.

For now, the work demonstrates the possibility of using AI to extract compact representations of dynamic electrons, "a goal of utmost importance for the success of cutting-edge quantum field theoretical methods for tackling the many-electron problem," the researchers conclude in their abstract.

The research was published in Physical Review Letters.

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Superconductivity Model With 100000 Equations Now Contains Just 4 Thanks to AI - ScienceAlert