Category Archives: Ai

Most businesses today have a great deal of data at their fingertips. They also have the tools to mine this information. But with this power comes responsibility. Before using data, technologists need to step back and evaluate the need. In todays data-driven, virtual age, it's not a question of whether you have the information, but if you should use it and how.

Artificial intelligence (AI) tools have revolutionized the processing of data, turning huge amounts of information into actionable insights. Its tempting to believe that all data is good, and that AI makes it even better. Spreadsheets, graphs, and visualizations make data real. But as any good technologist knows, the old computing sentiment, garbage in, garbage out still applies. Now more than ever, organizations need to question where the data originates and how the algorithms interpret that data. Buried in all those graphs are potential ethical risks, biases and unintended consequences.

Its easy to ask your technology partners to develop new features or capabilities, but as more and more businesses adopt machine learning (ML) operations and tools to streamline and inform processes, there is potential for bias. For instance, are the algorithms discriminating unknowingly against people of color or women? What is the source of the data? Is there permission to use the data? All these considerations need to be transparent and closely monitored.

The first step in this journey is to develop data privacy guidelines. This includes, for example, policies and procedures that address considerations such as notice and transparency that data is used for AI, policies on how information is protected and kept up to date, and how sharing data with third parties is governed. These guidelines hopefully build on an existing overarching framework of data privacy.

Beyond privacy, other relevant bodies of law may impact your development and deployment of AI. For example, in the HR space, it is critical that you refer to federal, state, and local employment and anti-discrimination laws. Likewise, in the financial sector, there are a range of applicable rules and regulations that have to be taken into account. Existing law continues to apply, just as it does outside the AI context.

Beyond existing law, with the acceleration of technology, including AI and ML, the considerations become more complex. In particular, AI and ML introduce new opportunities to discern insights from data that were previously unachievable and can do so in many ways better than humans. But AI and ML are created ultimately by humans, and without careful oversight there are risks of introducing unwanted bias and outcomes. Creating an AI and Data Ethics Board can help businesses anticipate issues in these new technologies.

Begin by establishing guiding principles to govern the use of AI, ML and automation specifically in your company. The goal is to ensure that your models are relevant and functional, and do not drift from their intended goal unknowingly or inappropriately. Consider these five guidelines:

1. Accountability and transparency. Conduct audit and risk assessments to test your models, and actively monitor and improve your models and systems to ensure that changes in the underlying data or model conditions do not inappropriately affect the desired results.

2. Privacy by design. Ensure that your enterprise-wide approach incorporates privacy and data security into ML and associated data processing systems. For example, do your ML models seek to minimize access to identifiable information to ensure that you are using only the personal data you need to generate insights? Are you providing individuals with a reasonable opportunity to examine their own personal data and to update it if its inaccurate?

3. Clarity. Design AI solutions that are explainable and direct. Are your ML data discovery and data usage models designed with understanding as a key attribute, measured against an expressed desired outcome?

4. Data governance. Understanding how you use data and the sources from which you obtain it should be key to your AI and ML principles. Maintain processes and systems to track and manage data usage and retention. If you use external information in your models, such as government reports or industry terminologies, understand the processes and impact of that information in your models.

5. Ethical and practical use of data. Establish governance to provide guidance and oversight on the development of products, systems and applications that involve AI and data.

Principles like these can both guide discussion about these issues and help to create policies and procedures about how data is handled in your business. More broadly, they will set the tone for the entire organization.

Guidelines are great -- but they need to be enforced to be effective. An AI and data ethics board is one way to ensure these principles are woven into product development and uses of internal data. But how can companies go about doing this?

Begin by bringing together an interdisciplinary team. Consider including both internal and external experts such as IT, product development, legal and compliance, privacy, security, audit, diversity and inclusion, industry analysts, external legal and/or an expert in consumer affairs, for instance. The more diverse and knowledgeable the team, the more effective your discussions can be around potential implications and the viability of different use cases.

Next, spend time discussing the larger issues. Its important here to step away from process for a minute and immerse yourselves in live, productive discussion. What are your organizations core values? How should they inform your policies around development and deployment of AI and ML? All this discussion sets the foundation for the procedures and processes you outline.

Setting a regular meeting cadence to review projects can be helpful as well. Again, the bigger issues should drive the discussion. For instance, most product developers will present the technical aspects -- such as how the data is protected or encrypted. The boards role should aim to analyze the project on a more fundamental level. Some questions to consider for guiding discussion could be:

Because AI and ethics has become an increasingly important issue, there are many resources to help your organization navigate these waters. Reach out to your vendors, consulting firms or trade groups and consortiums, like the Enterprise Data Management (EDM) Council. Implement the pieces that are appropriate for your business but remember that tools, checklists, processes, and procedures should not replace the value of the discussion.

The ultimate goal is to make these considerations a part of the company culture so that every employee that touches a project, works with a vendor or consults with a client, keeps data privacy front of mind.

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Why You Need an AI & Ethics Board - InformationWeek

AUSTIN, Texas, Jan. 24, 2022 /PRNewswire/ -- Silicon Labs, a leader in secure, intelligent wireless technology for a more connected world, today announced the BG24 and MG24 families of 2.4 GHz wireless SoCs for Bluetooth and Multiple-protocol operations, respectively, and a new software toolkit. This new co-optimized hardware and software platform will help bring AI/ML applications and wireless high performance to battery-powered edge devices. Matter-ready, the ultra-low-power BG24 and MG24 families support multiple wireless protocols and incorporate PSA Level 3 Secure Vaultprotection, ideal for diverse smart home, medical and industrial applications. The SoC and software solution for the Internet of Things (IoT) announced today includes:

"The BG24 and MG24 wireless SoCs represent an awesome combination of industry capabilities including broad wireless multiprotocol support, battery life, machine learning, and security for IoT Edge applications," said Matt Johnson, CEO of Silicon Labs.

First Integrated AI/ML Acceleration Improves Performance and Energy Efficiency

IoT product designers see the tremendous potential of AI and machine learning to bring even greater intelligence to edge applications like home security systems, wearable medical monitors, sensors monitoring commercial facilities and industrial equipment, and more. But today, those considering deploying AI or machine learning at the edge are faced with steep penalties in performance and energy use that may outweigh the benefits.

The BG24 and MG24 alleviate those penalties as the first ultra-low powered devices with dedicated AI/ML accelerators built-in. This specialized hardware is designed to handle complex calculations quickly and efficiently, with internal testing showing up to a 4x improvement in performance along with up to a 6x improvement in energy efficiency. Because the ML calculations are happening on the local device rather than in the cloud, network latency is eliminated for faster decision-making and actions.

The BG24 and MG24 families also have the largest Flash and random access memory (RAM) capacities in the Silicon Labs portfolio. This means that the device can evolve for multi-protocol support, Matter, and trained ML algorithms for large datasets. PSA Level3-Certified Secure VaultTM,the highest level of security certification for IoT devices, provides the security needed in products like door locks, medical equipment, and other sensitive deployments where hardening the device from external threats is paramount.

To learn more about the capabilities of the BG24 and MG24 SoCs and view a demo on how to get started, register for the instructional Tech Talk "Unboxing the new BG24 and MG24 SoCs" here: https://www.silabs.com/tech-talks.

AI/ML Software and Matter-Support Help Designers Create for New Innovative Applications

In addition to natively supporting TensorFlow, Silicon Labs has partnered with some of the leading AI and ML tools providers, like SensiML and Edge Impulse, to ensure that developers have an end-to-end toolchain that simplifies the development of machine learning models optimized for embedded deployments of wireless applications. Using this new AI/ML toolchain with Silicon Labs's Simplicity Studio and the BG24 and MG24 families of SoCs, developers can create applications that draw information from various connected devices, all communicating with each other using Matter to then make intelligent machine learning-driven decisions.

For example, in a commercial office building, many lights are controlled by motion detectors that monitor occupancy to determine if the lights should be on or off. However, when typing at a desk with motion limited to hands and fingers, workers may be left in the dark when motion sensors alone cannot recognize their presence. By connecting audio sensors with motion detectors through the Matter application layer, the additional audio data, such as the sound of typing, can be run through a machine-learning algorithm to allow the lighting system to make a more informed decision about whether the lights should be on or off.

ML computing at the edge enables other intelligent industrial and home applications, including sensor-data processing for anomaly detection, predictive maintenance, audio pattern recognition for improved glass-break detection, simple-command word recognition, and vision use cases like presence detection or people counting with low-resolution cameras.

Alpha Program Highlights Variety of Deployment Options

More than 40 companies representing various industries and applications have already begun developing and testing this new platform solution in a closed Alpha program. These companies have been drawn to the BG24 and MG24 platforms by their ultra-low power, advanced features, including AI/ML capabilities and support for Matter. Global retailers are looking to improve the in-store shopping experience with more accurate asset tracking, real-time price updating, and other uses. Participants from the commercial building management sector are exploring how to make their building systems, including lighting and HVAC, more intelligent to lower owners' costs and reduce their environmental footprint. Finally, consumer and smart home solution providers are working to make it easier to connect various devices and expand the way they interact to bring innovative new features and services to consumers.

Silicon Labs' Most Capable Family of SoCs

The single-die BG24 and MG24 SoCs combine a 78 MHz ARM Cortex-M33 processor, high-performance 2.4 GHz radio, industry-leading 20-bit ADC, an optimized combination of Flash (up to 1536 kB) and RAM (up to 256 kB), and an AI/ML hardware accelerator for processing machine learning algorithms while offloading the ARM Cortex-M33, so applications have more cycles to do other work.Supporting a broad range of 2.4 GHz wireless IoT protocols, these SoCs incorporate the highest security with the best RF performance/energy-efficiency ratio in the market.

Availability

EFR32BG24 and EFR32MG24 SoCs in 5 mm x 5 mm QFN40 and 6 mm x 6 mm QFN48 packages are shipping today to Alpha customers and will be available for mass deployment in April 2022. Multiple evaluation boards are available to designers developing applications.Modules based on the BG24 and MG24 SoCs will be available in the second half of 2022.

To learn more about the new BG24 family, go to: http://silabs.com/bg24.

To learn more about the new MG24 family, go to: http://silabs.com/mg24.

To learn more about how Silicon Labs supports AI and ML, go to: http://silabs.com/ai-ml.

About Silicon Labs

Silicon Labs (NASDAQ: SLAB) is a leader in secure, intelligent wireless technology for a more connected world. Our integrated hardware and software platform, intuitive development tools, unmatched ecosystem, and robust support make us an ideal long-term partner in building advanced industrial, commercial, home, and life applications. We make it easy for developers to solve complex wireless challenges throughout the product lifecycle and get to market quickly with innovative solutions that transform industries, grow economies, and improve lives.Silabs.com

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Silicon Labs Brings AI and Machine Learning to the Edge with Matter-Ready Platform - inForney.com

There is great promise and potential in artificial intelligence (AI), but if such technologies are built and trained by humans, are they capable of bias?

Absolutely, says William Wang, the Duncan and Suzanne Mellichamp Chair in Artificial Intelligence and Designs at UC Santa Barbara, who will give the virtual talk What is Responsible AI, at 4 p.m. Tuesday, Jan. 25, as part of the UCSB Librarys Pacific Views speaker series (register here).

The key challenge for building AI and machine learning systems is that when such asystem is trained on datasets with limited samples from history, they may gain knowledge from the protected variables (e.g., gender, race, income, etc.), and they are prone to produce biased outputs, said Wang, also director of UC Santa Barbaras Center for Responsible Machine Learning.

Sometimes these biases could lead to the rich getting richer phenomenon after the AI systems are deployed, he added.Thats why in addition to accuracy, it is important to conduct research in fair and responsible AI systems, including the definition of fairness, measurement, detection and mitigation of biases in AI systems.

Wangs examination of the topic serves as the kickoff event for UCSB Reads 2022, the campus and community-wide reading program run by UCSB Library. Their new season is centered on Ted Chiangs Exhalation, a short story collection thataddressesessential questions about human and computer interaction, including the use of artificial intelligence.

Copies of Exhalation will be distributed free to students (while supplies last) Tuesday, Feb. 1 outside the Librarys WestPaseo entrance. Additional events announced so far include on-air readings from the book on KCSB, a faculty book discussion moderated by physicist and professor David Weld and a sci-fi writing workshop. It all culminates May 10 with a free lecture by Ted Chiang in Campbell Hall.

First though: William Wang, an associate professor of computer science and co-director of the Natural Language Processing Group.

In this talk, my hope is to summarize the key advances of artificialintelligence technologies in the last decade, and share how AI can bring us an exciting future, he noted. I will also describe the key challenges of AI: how we should consider the research and development of responsible AI systems,which not only optimize their accuracy performance,but also provide a human-centric view to consider fairness, bias, transparency and energy efficiency of AI systems.

How do we build AI models that are transparent? How do we write AI system descriptions that meet disclosive transparency guidelines?How do we consider energy efficiency when building AI models? he asked. The future of AI is bright, but all of these are key aspects of responsible AI that we need to address.

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Getting a Read on Responsible AI | The UCSB Current - The UCSB Current

In the last decade, almost every argument putting forward the idea of artificial intelligence (AI) taking up white-collar jobs from humans was downplayed by critics with a rather simplistic counter machines lack the creativity and intuition of a human worker.

For instance, Rytr, a popular AI copywriting app with 6,00,000 users, is helping businesses with text that is indistinguishable from human writing. Unlike content writers, this app doesnt tire, doesnt ask for payment and can generate an unlimited amount of content.

Similarly, AI-powered customer service is slowly taking over the market. In the United States, an estimated 85 percent of customer interaction is taking place with AI-backed tools. Given the brisk pace at which such tools are gaining popularity, there would be huge implications for nearly 3 million customer service representatives employed in the US.

The landscape has clearly changed. The businesses which dont use AI tools are at a disadvantage. Their input cost is higher and their product (or services) is probably inferior to their counterparts who have deployed modern technologies.

While AI-backed technologies are a boon for nations like Japan where the workforce is ageing, they can lead to a massive disruption in countries like India, where millions of people enter the job market every year.

Nonetheless, AI eating up jobs is surely not new but a reality that we all have to come to terms with. Of course, there is this argument of new job avenues opening up with the rise of AI but the question is are we prepared?

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(Edited by : Thomas Abraham)

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How is AI slowly eating up white-collar jobs? - CNBCTV18

Did you miss a session from the Future of Work Summit? Head over to ourFuture of Work Summit on-demand libraryto stream.

This article was contributed by Taesu Kim, CEO of Neosapience

With the tremendous advances in how AI/ML technologies are being deployed, one of the most exciting, controversial, and rapidly evolving advances relates to human voice. One particular example jumps out as encapsulating the complex of issues and emotions tied to AI-powered voices.

Last summer, AI technology was used to give voice to some of the late Anthony Bourdains writings, words that he never spoke or read aloud but were nevertheless his; voice cloning technology brought the text to life in Roadrunner: A Film About Anthony Bourdain. Some in the audience felt duped that it wasnt really Bourdain, others thought the move was a misstep as Bourdain was not alive to give permission to manipulate his voice in such a way, while many felt it was simply a creative storytelling device.

The Bourdain example highlights two key issues that will rise to the forefront of how AI-based voice technologies will be used in the future. On one hand, there are questions about who has ownership of a voice, and therefore control over how it might be used now and in the future. On the other is the ethical issue: is it morally right to allow someones voice to be used in the public domain after his or her death when he or she has no control over how it will be used or what is said?

The 2nd Annual GamesBeat and Facebook Gaming Summit and GamesBeat: Into the Metaverse 2

These questions are surfacing because AI-based voice technology is beginning to really take off; there has been a tremendous amount of time and money spent on research and development to make machine-generated voices sound real. They are now capable of conveying the emotions, texture, cadence as well as the natural rise and fall and many other distinct markers that characterize human speech (not to mention song). This is game-changing, because it has become hard for listeners to determine the difference between the speech of a human and that of a machine.

As such, weve reached a defining moment in the technologys development where we need to figure out basic guidelines and set guardrails, or like so many technologies before it, voice technologys applications will be used in ways they were never intended.

Weve become a global society thirsting for rich content experiences whether its through film, television, and streaming services or user-generated mediums like YouTube and TikTok. And soon the metaverse will offer even more new ways to engage with content. All of these avenues present enormous opportunities for AI-powered voice, as well as video. AI-powered voice and video make it exponentially faster, easier, and less expensive to create content, not to mention adapt it for other languages. These technologies are also highly accessible through text-to-voice services, so essentially anyone can leverage AI for content creation without requiring a studio and a lot of fancy equipment, spurring high demand in the entertainment industry.

At the same time, there is a lot of fear surrounding the ownership and monetization of ones virtual identity. In a world of deep fakes, misrepresentation, and identity theft, it is fair for individuals to wonder what happens if someone co-opts their digital identity for their own purposes. Not only would the individual lose control of how his or her likeness is deployed, as well as any revenue or brand recognition associated with it, but it could be used in inappropriate, even illegal ways or so the thinking goes.

This is highly unlikely, however. Each human voice as well as face has its own unique footprint, comprised of tens of thousands, to millions, of characteristics. With advanced fraud detection and management technologies in development, AI-powered identities can be safeguarded relatively easily. What is far more complicated, however, is managing that digital identity over time. It becomes not just about business, but a series of ethical decisions that are inextricably intertwined.

Was it okay for the director to use Bourdains digitized voice in his movie? The director allegedly obtained permission to use his AI-cloned voice to deliver the lines in question, but from whom? Who ultimately holds the right to decide?

Similarly, famous South Korean folk rock singer Kim Kwang-Seoks AI-powered voice recently was used to release a new song. The artist has been dead for 25 years, but a broadcasting company brokered a deal with the artists family to use AI to clone his voice and deploy it for something entirely new, largely to the delight of the public. There are many other cases of entertainment companies and content creators seeking to bring the voice and likeness of famous people back for concerts or movies. But is it ethically responsible?

On the surface, it is something that can be addressed simply enough through licensing deals and contracts with the entertainers estate or, ideally, determined while the artist is still alive. As the practice becomes more common, we should be prepared to see a sort of name, image, voice, likeness clause within a persons Will, particularly one that governs their posthumous wishes or appoints a manager for overseeing the career of their virtual self much the same way they have a business manager in life.

It is one thing for celebrities to consider such content and management deals, but what about regular, everyday people? Perhaps those who grieve for loved ones, like this woman who lost her young daughter due to an illness? Meeting in a virtual reality environment, the woman was able to connect with her daughter in avatar form, seemingly traveling to a version of heaven and holding a birthday party. The experience is clearly quite meaningful to the young mother and her family, but the interaction is in no way real. Some companies as well as consumers want no part in developing such experiences because it takes liberties with the childs likeness and personality, while others see a chance to provide comfort and closure to families in pain.

And what about creating new virtual experiences for education purposes, such as the award-winning Interactive Holograms: Survivor Stories Experience? At a time when students and citizens question whether the Holocaust was real or what being actually Nazi entailed, is there not room to use such technology for good? What lines are appropriate in terms of creative license?

There are no easy answers when it comes to a virtual, or ai-powered identity. We sit at the precipice of an entirely new means of content creation, where famous as well as regular people will soon be asked to think about how their voice and image could be used not just today but long after they are gone.

Virtual identity will become a currency that should be regarded similar to their physical assets, one in which they can specify their wishes in life and death, and appoint managers and executors to approve its usage moving forward. This may sound far-fetched, but digital voices dont age, nor do avatars. With the metaverse going mainstream, our virtual selves can live well beyond our years.

It will become a new imperative that everyone determines and clearly defines parameters they are comfortable with in terms of their digital identity. Similarly, the companies that offer platforms for AI-powered voice and video creation need to develop clear policies for the adoption and use of a specific virtual ai-powered identity. Doing so protects both individuals and companies from tumbling down a slippery slope as highly disruptive AI-powered virtual identities become normalized.

Taesu Kim is the CEO of Neosapience

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To increase the business revenue, here are the top AI Strategies that you all should follow in 2022

Artificial intelligence (AI) is poised to redefine how businesses work, healthcare, and many other areas. Already it is unleashing the power of data across a range of crucial functions such as customer service, marketing, training, pricing, security, surgeries, automobiles, and so on. To remain competitive, firms in nearly every industry will need to adopt AI. The role of AI in businesses has modified considerably from its preliminary creation on the threshold of an enterprise in their innovation labs to the modern-day, while human beings are starting to recognize that it has the ability to convert businesses from the center out.

According to the reports, Artificial Intelligence will be better than human beings in translating languages by 2024, promoting items by 2031, and conducting surgical procedures by 2053. Businesses are using AI to enhance the productiveness of their employees. The advantage of AI for enterprise is that it handles repetitive tasks throughout an organization simply so that employees can focus on creative solutions, complicated problem solving, and impactful work. The idea of the workplace may also be redefined through the arrival of technology. The future work may be distinctly flexible. The concept of Work from Home may be the brand-new norm and virtual meetings and conferences will be the normal practice. There are many AI Strategies that will help enterprises increase their revenue. These strategies will not just maximize the revenue but also it will help the businesses to reduce spending additional capital on many things such as cyber-attacks, repetitive tasks, and the list will go on. Here are the top AI strategies for businesses that will help your organization increase profits.

The AI collects actionable insights from numerous data reasserts and structures to assist with root motive evaluation. This will reduce attacks from outsiders and it will help the company to protect its data and crucial information.

The demand for AI skills has grown faster and its difficult to imagine a business that wouldnt benefit from the detailed analysis of AI and machine learning algorithms performantly. It automates the process of applying machine learning techniques to data. Usually, a data scientist would spend their time on pre-processing, selecting features, selecting and tuning models, and then evaluating the results. AutoML will be able to automate tasks (such as preprocessing, selecting features, selecting and tuning models, evaluating results, etc.) and can provide high-performing results to certain problems.

While data is the most appetizing target for every cyber-attack, companies can also use data against them via advanced data analytics consisting of machine learning (ML), artificial intelligence (AI), statistics visualization, and so forth. This will help enterprises from spending much on problem-solving or from robing business data.

Consumers usually look around and compare different options while purchasing. They even go through social media handles of businesses and demand special treatment. AI allows a business organization to get through such customers in a far-reaching manner. This allows a business to engage in a real-time, one-on-one conversation with consumers.

Data automation is the process of uploading, handling, and processing data via automated tools, instead of manually performing all these tasks. It enables the organization to manage big data and innovate the pace of business. The data collected across an organizations business units, applications, and external sources is growing exponentially. Big data is crucial for faster, more informed decision-making.

The consensus amongst many specialists is that some professions might be definitely computerized. A group of senior-level tech executives who contain the Forbes Technology Council named 13, which include insurance underwriting, warehouse, and production jobs, consumer service, studies and information entry, long haul trucking, and a fairly disconcertingly wide class titled Any Tasks That Can Be Learned. Accountants, manufacturing unit employees, truckers, paralegals, and radiologists simply to name a few.

Chatbots have been around for ages. But with the evolution of AI, chatbots have ended up being more in time-saving, process-efficiency-riding technology play.

With time, chatbots at the moment are smarter with the aid of using distinctive features of understanding and experience, and its application. Responses from chatbots are consequently more meaningful, and the performance of communications with clients and people has improved.

Chatbots can act as a scalable way for bridging the distance among people and management, with the aid of using making data more effortlessly available to people.

Chatbots also are being used to survey people, in a real-time, issue-particular format. This is a notable improvement for the efficient checking out the group of workers pulse, employee engagement, and well-known place of job sentiment, a system that formerly could have taken weeks to complete. So, on this application, ability problems may be diagnosed and addressed earlier than they turn out to be the foremost troubles for the organization.

For instance, writing jobs. AI can effortlessly generate textual content which is indistinguishable from human writing. This form of AI job automation is helping enterprises to maximize their revenue. Its anticipated that robots dont tire, dont require payment, and may generate a vast quantity of content material.

For example Ryte, Writesonic, Article Forge.

This AI isnt restrained to writing. AI is likewise automating jobs in customer service, accounting, and a bunch of different professions. For instance, groups like Thankful, Yext, and Forethought use AI to automate client support. This shift is frequently imperceptible to the client, who doesnt recognize if theyre talking to a biological intelligence or a machine. The upward push of AI-powered customer service has huge implications for the workforce. Its anticipated that 85 percent of client interactions are already dealt with without human interaction.

It automates redundant tasks and helps in keeping workers happy also it has high computational power. Fear for job security often comes intertwined with mentioning AI in the context of the workplace. It has been predicted that AI will render various jobs redundant and replace half of the workforce by 2033. It will replace many jobs, but at the same time, it will create many new, more fulfilling jobs. While AI directly improves employee productivity by automating redundant and mundane tasks, it indirectly helps with increased work satisfaction in employees. AI tools have been deployed to analyze the emojis and certain keywords used on workplace communication platforms like Slack, so using these, you can subjectively gauge employee satisfaction in your workplace.AI is superior to its human counterpart when it comes to solving complex problems and recognizing patterns. For instance, we have AI programs that can scan complex legal documents to fetch relevant information. This frees employees to focus on tasks that add value to your business

AI creates targeted marketing strategies. AI can be leveraged to analyze data and identify patterns in consumer behavior in your sales funnel faster and with more accuracy than a human. Post-processing, your sales team will be left with only high-quality leads, which wont require much vetting. A solid marketing strategy is a core to the success of any organization, and targeting your campaigns to reach the right audience will give you the best results. AI-powered marketing tools like chatbots, advertising platforms, and data-collection systems will be helpful in this venture. AI can also generate personalized content based on customer data in the CRM, and this will further help in automating a sales pursuit.

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Top 10 AI Strategies that Will Help Increase the Business Revenue - Analytics Insight

AI failure

The more a technology or concept permeates and gets normalized in our day-to-day lives, the more we grow to expect from it. About two decades ago, a sub-56kpbs dial-up internet connection seemed miraculous. Today, with internet speeds as high as 2000Mbps becoming normal, the 56Kbps connection would be considered a failure of sortsin the developed world, at least. This shift in expectation also applies to AI. Having seen numerous practical AI applications aid human convenience and progress, both the general population and the AI research community now expects every new breakthrough in the field to be more earth-shattering than the previous one. Similarly, what qualifies as AI failure has also seen a massive shift in recent years, especially from a problem owners perspective.

Just the fact that an AI model performs a specific function with expected levels of efficacy is no longer the only requisite for its applications to be considered successful. These systems must also be able to provide significant real-world gains in the form of time saved or revenue earned. For instance, a smart parking system that can predict parking availability with 99.7% accuracyalthough undoubtedly efficaciouscannot be considered successful if its real-world adoption does not lead to tangible gains. Even with such a system installed, parking lot managers or smart city administrators may not be able to make optimal use of their parking spaces due to a number of reasons. These could vary from simple causeslike parking lot operators not being able to use the software interface optimallyto complicated oneslike patrons and drivers struggling or hesitating to adapt to the new system. Due to such reasons and many others, only a fraction of AI projects are ever successful. The estimates for the total percentage of AI projects that fail to deliver real value range from 85% to 90%.

In most of these cases, the lack of tangible results achieved by AI systems has much less to do with the technological aspect and more to do with the human aspect of these systems. The success and failure of these projects depend on how the people interact with the technologies to achieve intended objectives.

As researchers continue to work and add to the body of AI research, the effectiveness of AI and AI-driven systems is constantly increasing. However, as powerful as it may be, any AI-driven tool is just thata tool. The success and failure of AI initiatives, more often than not, are determined by how the usersboth primary and secondaryperceive, receive and operate these AI systems.

Business leaders such as owners, directors and C-suite executivesoften end up being only secondary users of AI, or any other technological application for that matter. However, they are among the bigger beneficiaries as well as the biggest enablers of such initiatives. After all, it is often their will and wherewithal which matters while driving AI initiatives. So, the most common reasons for AI initiatives not delivering real value often involve a lack of buy-in from business leaders. Buy-in does not necessarily mean just a willingness to dispense funds for AI initiatives. An increasing number of businesses are investing in AI initiatives anyway, which means that AI failure does not necessarily stem from an absence of investment.

Today, buy-in is represented by a total conviction in a technology or investments ability to make an impact. This conviction results in a commitment to making these technological endeavors successful through means that involve more than just the technology itself. For instance, a business truly committed to the success of its AI initiatives will also invest in the non-core aspects of the initiatives, such as safety and privacy, among others. Ultimately, it is this commitment that ensures that they take all necessary steps to ensure AI success.

More often than not, AI-based applications do not entirely automate manual processes. They only automate the most analysis-intensive tasks. This means that human operators are necessary to leverage and augment the data processing capabilities of AI. This makes the role of human users extremely important for these AI applications.

Even the best AI-enabled business intelligence tools will prove useless if the executives using them arent trained to navigate the dashboards or to understand the data. This problem becomes even more pronounced where AI tools are involved at an operations level, such as computer vision-based handheld vehicle inspection tools or a mobile parking app that users can use to find and book parking spots. When the users are not trained enough to be able to navigate and use technological interfaces, the applications may not deliver the expected outcomes. Although a well-designed User Experience (UX) can go a long way in these circumstances, it is equally crucial for users to be educated about these applications.

Before practical training on how to use new AI applications, users should be given awareness training on how the new technology will add value to their work. More importantly, they should be convinced that the objective of technology is not to replace them but to augment their efforts. Thats because the fear of obsolescence is among the biggest underlying reasons for low user adoption.

Be it consciously or subconsciously, many workersmost of whom are potential AI usersfear becoming obsolete as AI becomes more commonplace. This perception of threat often manifests itself as an unwillingness to adopt the technology. The lack of enthusiasm then leads to a lack of involvement in training, which ultimately hampers the results of AI initiatives.

AI initiatives will only become successful and deliver significant ROI when all the usersfrom top executives to blue-collar workersare educated not about the technology but also their roles in .

Most AI applications are bespoke solutions to problems that are specific to the companies and customers using them. This means that there isnt a fixed playbook on coexisting with and using AI tools. Hence, it is unreasonable to expect the users of AI solutions to educate themselves on their organizations AI initiatives. Businessesalong with the AI implementation partnersmust come together to create case-specific user education strategies for the entire lifecycle of the AI solutions. By creating and executing these user education strategies, businesses can ensure that their people facilitate AI initiatives in more ways than one.

Why User Education Is Necessary To Avoid AI Failure

Before even an AI project starts, it is imperative to ensure that the top leadership of the organization is on board with the project. And that is exactly what top-level user education achieves. When business leaders and leading investors are aware of what results to expect from proposed AI initiatives, they are more comfortable investing in the same. However, it is equally important to establish expectations in terms of the input and support that will be required from the leadership in making an AI initiative a success. By creating awareness regarding the potential outcomes and expected support will ensure that AI projects have the structural support to be sustainable and successful. Making top-level decision makers aware of challenges will also minimize the chances of them withdrawing support when projects run into obstacles.

In addition to making the secondary users aware of the potential benefits of AI initiatives, it is crucial to make sure that the primary users are not just accepting but enthusiastic about the adoption of AI. At the end of the day, if the end users do not use the technology the way it is supposed to be used, the technology will never be able to deliver on expectations. So, part of the expectations from top-level leadership should be convincing the lower-level managers and employees of the value of proposed AI initiatives. The leaders can do this by first establishingthrough open and clear communicationthat the AI applications will not replace the human workforce but will augment it. Another way the leadership can accelerate user adoption is by providing adequate reskilling opportunities to employees so that they can be better operators of AI tools. Moreover, translating the broader advantages of the new AI solutions into individual benefits for workers in different roles will ensure that workers welcome the infusion of AI into daily operations.

Practical training on how to use technology should constitute the final leg of the user education strategy. Once the leadership and the end users are motivated enough to use the new AI solutions, they will be more receptive to instructions of use. As a result, they will be better able to contribute to AI initiatives and participate in their success.

This process of user education should not be viewed as a linear, one-time activity aimed at mitigating AI failure. It should be considered as a cycle that begins with the discovery of new applications and ends with these applications becoming an integral, value-adding part of regular business operations. Businesses aiming to implement AI in the near future can get started right now by educating their people on why they shouldnt view the AI-driven future with fear but with hope.

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Why User Education Is Necessary To Avoid AI Failure - Forbes

LAGUNA HILLS, Calif.--(BUSINESS WIRE)--BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY) is a leading provider of ultra-low power, high performance artificial intelligence technology and the worlds first commercial producer of neuromorphic AI chips and IP. BrainChip is looking forward to 2022 as it closes its most successful year ever buoyed by technological advancements made to its Akida technology, commercialization, additions of veteran leadership to both its management and Board of Directors, market exchange upgrades and more.

BrainChip saw its vision for brain-inspired Neuromorphic AI architecture move into production reality this year. This technology, which mimics the processing function and learning structure of the human brain, helps customers create ultra-low power products with the ability to perform classification entirely on-chip and to rapidly learn on-chip without the need to connect to the cloud.

Among the many milestones achieved this year, the Akida AKD1000 neuromorphic processor production chips were received from BrainChips manufacturing partner, SocioNext America and TSMC. BrainChip completed functionality and performance testing of the production chips and began volume production. This success enabled the company to start accepting and shipping orders of Akida development kits to its partners, large enterprises and OEMs for their own internal testing, validation, and product development. Additionally, BrainChip licensed its Akida IP to ASIC industry heavyweights MegaChips and Renesas to help enhance and grow their technology positioning for next-generation, cloud independent AI products.

This year also saw the introduction of MetaTF, a versatile ML framework that works within TensorFlow1, which allows people working in the convolutional neural network space to seamlessly transition to neuromorphic computing quickly and easily without having to learn anything new. The MetaTF development environment is an easy-to-use machine learning framework for the creation, training and testing of neural networks, supporting the development of systems for Edge AI on BrainChips Akida AKD1000 event domain neural processor. Over 4,500 potential customers have started to use MetaTF in 2021 alone.

Achieving rapid success in Akida product development allowed BrainChip to move its US headquarters to larger facilities to support expected customer growth as the company continues to move toward commercialization of its Akida AKD1000 neuromorphic processor and comprehensive development environment. The new 10,000 sq. ft. (929 square meters) facility is four times the size of its previous headquarters and provides the company with the ability to scale its services and processes needed to satisfy expected customer and support infrastructure needs.

BrainChip recently announced the appointment of Mr. Sean Hehir as BrainChips new CEO. He takes over from interim CEO Peter van der Made, who moves to his previous CTO position full time. Mr. Hehirs focus is to guide BrainChips progress towards full commercialization of the Akida AKD1000 chip and its IP. He is joined by non-executive director additions to the Companys Board former ARM executive Antonio J. Viana and innovation champion and strategic advisor Ms. Pia Turcinov.

As a public company, BrainChip received several upgrades to its market presence, which included its addition to the S&P/ASX 300 index, an update of its ticker symbol on the OTC market and a listing upgrade to the OTCQX Best market. BrainChip also launched a US based ADR (BCHPY) that allows BrainChip to continue its path of pursuing high accessibility to the US capital markets. Due to the strong demand for high-growth potential, Artificial Intelligence stocks in the US are expected to result in an influx of US investment and ultimately an increase in shareholder value. The companys successes have driven a rise of its stock price by 100 percent over the last 12 months

Other highlights from this year include being named among the EE Times Silicon 100, the launch of five sensor modalities of Akida (odor, vision, audio, tactile and gustation, which see real-world application in Covid-19 detection, facial recognition, voice recognition, vibration analysis and wine and beer taste recognition). Practical demonstrations in quality control and other industrial environments consume only microwatts to milliwatts of power. BrainChips founder Peter van der Made won the AI Hardware 2021 innovator award based on the development and production capabilities of Akida, the continuation of its highly popular This is Our Mission podcasts, and frequent speaking appearances at shows and events throughout the world.

One of the things that impressed me the most about BrainChip when looking to join the company was the quality and volume of successes it has achieved, and the dedicated and talented team that are the heart of BrainChips success, said Mr. Hehir. As the company transfers from strength of vision to strength of production, the possibilities are endless. BrainChip is moving to market readiness, expansion of a product portfolio, improvements to human resources, and improvements in the stock market. Im excited to see Akidas impact on the $46B USD Edge AI total addressable market (TAM) as BrainChip has unequivocally proved that it is the leader in the neuromorphic AI space. I look forward to seeing the kind of revolutionary moves we make in 2022.

About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)

BrainChip is a global technology company that is producing a groundbreaking neuromorphic processor that brings artificial intelligence to the edge in a way that is beyond the capabilities of other products. The chip is high performance, small, ultra-low power and enables a wide array of edge capabilities that include on-chip training, learning and inference. The event-based neural network processor is inspired by the spiking nature of the human brain and is implemented in an industry standard digital process. By mimicking brain processing BrainChip has pioneered a processing architecture, called Akida, which is both scalable and flexible to address the requirements in edge devices. At the edge, sensor inputs are analyzed at the point of acquisition rather than through transmission via the cloud to a data center. Akida is designed to provide a complete ultra-low power and fast AI Edge Network for vision, audio, olfactory and smart transducer applications. The reduction in system latency provides faster response and a more power efficient system that can reduce the large carbon footprint of data centers.

Additional information is available at https://www.brainchipinc.com

Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006

1 TensorFlow is a registered trademark of Google LLC.

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BrainChip Reflects on a Successful 2021, with Move to Market Readiness Behind Next-Generation Edge-Based AI Solutions - Business Wire

The impact of 5G

The commercial roll out of 5G is now under way. But simply put, 5G is not just another G. Its a complete ecosystem change in the way networks are run and managed, including how applications run on the network.

There are three main use case groups in 5G:

Other, emerging use case groups include massive machine type communication, or MTC.

This is where the connectivity and density of 5G really comes into play.

MTC enables the connectivity of a huge number of devices millions, billions of devices in fact, all of which are connected. Although theyre more likely to send very low data rates, the number of devices, and their long battery life means they can open the doors to brand new industrial use cases. For example, monitoring, farming, agriculture, transportation, automotive, smart cities, and healthcare could all transform thanks to MTC. Its all about connecting human expertise to a huge number of connected sensors for faster, more efficient insights.

Another emerging technology is ultra-reliable, low latency communications, or URLLC. This is where 5G shines. Use cases with URLLC can deliver very low latencies, down to one millisecond, which is a perfect solution for mission-critical use cases from vehicle to vehicle, remote diagnostics, or remote surgery.

URLLCs low latency is perfect for mission-critical use cases such as those in healthcare.

When it comes to 5G networks, AI is no longer a nice to have, but a must-have component to tackle the tremendous complexity that comes with 5G. AI along with the data and automation capabilities that come with it supports the diverse ecosystem of evolving networks in a way that humans alone are unable to manage.

The expectations of 5G are high due to its potential to transform industries. Service providers expect high performance, low latency, throughput and availability that 5G promises. As a result, the ability to operate 5G networks will need to speed up in fact, the development of high-level operational capabilities like zero-touch and self-healing networks are already in the works to meet this growing demand.

The evolution of networks involves some tough challenges, the first of which is data, particularly, how to shape network operations to be data-centric and data-driven. For example, the data elements within a 5G network are highly distributed. It comes in all shapes, sizes, and volumes. So how is it possible to efficiently manage this data? After all, data is what drives capabilities like machine learning and advanced analytics. Without it, we cant run future networks.

First, a clearly defined and executed data-driven strategy is crucial for service providers; one that drives how data should be managed across operations end-to-end, from ingestion all the way to final decision making.

Second, clear decisions need to be made around where and how data is processed, so AI logic can make timely decisions. For example, data could be transferred to a centralized cloud location to be processed for AI inference, but that may incur high transfer costs and additional delays especially for real-time use cases where decisions must be made in a split second. Instead, AI inference could be moved closer to the data source as well as creating a shorter and leaner pipeline.

Another important aspect is to ensure data quality and lineage, end-to-end, so decisions can be made based on trustworthy and high-quality data input. It makes no sense to rely on an AI logic if the data is corrupt.

And finally, organizational transitions regarding competence, technology development, and future-proofing employees skills are all additional challenges that can occur with 5G and AI adoption.

To overcome these new challenges, Ericsson changed its approach from being reactive to becoming much more proactive and predictive, which is the baseline of our AI modeling. The result is a model called the Ericsson Operations Engine. In parallel to our data-driven approach, were also upskilling our people who can see the network from an end-to-end perspective.

We also focus on data analytics, competency development, and 5G technologies, along with developing specific use case experts to support new industry requirements. We need to have the competence to understand the whole ecosystem of these emerging use cases not just an understanding of the technology, but also the various platforms and tools to help run network operations in a smoother and more automated way.

As service providers begin to offer 5G services to enterprises, the efficient use of the network will be crucial for them to keep costs down. This is where network slicing comes in.

Network slicing is a unique technology in 5G, where the network can be logically sliced end-to-end to deliver customizable service performance. Service providers will be able to slice the network into segments and offer different segments of the network to different enterprise customers and make sure they receive the performance level they're paying for.

Of course, slicing comes with its own challenges, including its technical complexity and the fact that it's cross domain. Consequently, we've been working on several advanced AI techniques to help customers prepare for the challenge of operating such complex systems. The future of networks ultimately lies in cognitive systems, where networks can apply a combination of machine learning and machine reasoning, which is built from knowledge base and reasoning engines to generate conclusions.

This technique is not only relevant to network slicing, but any complex network operations, because we often don't have enough data, or enough labels to train all the possible scenarios. This approach, however, enables the machine to learn on its own and make critical decisions itself without previously being trained or told to do so.

We believe this entire approach, along with intent-based operations, will be a critical step in making 5G operations as autonomous as possible. Exciting times are just around the corner.

This is how network operations can make 5G systems resilient.

Read all about driving 5G monetization through intent-based network operations.

Read more about Managed Services.

The rest is here:

What is the relationship between AI and 5G? - Ericsson

Late last year, China's Ministry of Science and Technologyissued guidelines on artificial intelligenceethics.The rulesstress user rights and data control while aligning with Beijing's goal of reining in big tech.China is now trailblazing the regulation of AI technologies, and the rest of the world needs to pay attention to what it's doing and why.

The European Union had issueda preliminary draft of AI-related rules in April 2021, but we've seen nothing final. In the United States, the notion of ethical AIhas gotten some traction, but there aren't any overarching regulations or universally accepted best practices.

China'sbroad guidelines, which are currently in effect,are part of the country's goal of becoming the global AI leader by 2030; they alsoalign with long-term social transformations that AI will bring and aim tofill the role of proactively managing and guiding those changes.

The National New Generation Artificial Intelligence Governance Professional Committeeis responsible for interpreting the regulationsand will guide their implementation.

Here are the most important parts of the directives.

Titled "New Generation Artificial Intelligence Ethics Specifications,"the guidelines list six core principles to ensure "controllable and trustworthy" AI systems and, at the same time, illustrate the extentof theChinese government's interest in creatinga socialist-driven and ethically focused society.

Here are the key portions of the specs,which can be useful in understanding the future direction of China'sAI.

The aim is to integrate ethics and morals into the entire lifecycle of AI;promote fairness, justice, harmony, and safety;and avoid issues such as prejudice, discrimination, and privacy/information leakage. The specification applies to natural persons, legal persons, and other related institutions engaged in activities connected to AI management, research and development, supply, and use.

According to the specs, the various activities of AI "shall adhere to the following basic ethical norms":

These ethical rules should be followed in AI-specific activities around themanagement, research and development, supply, and use ofAI.

The regulation specifies these goals when managing AI-related projects:

Under the rules, companies willintegrate AI ethics into all aspects of their technology-related research and development. Companies are to "consciously" engage inself-censorship, strengthen self-management, and refrain from engaging in any AI-relatedR&D that violates ethics and morality.

Another goal relates to improvedquality fordataprocessing, collection, and storage and enhanced security and transparency in terms of algorithm design, implementation, and application.

The guidelines also require companies to strengthen quality control by monitoring and evaluating AIproducts and systems.Related to this are the requirementsto formulate emergency mechanisms and compensation plans or measures, tomonitor AI systems in a timely manner, and toprocess user feedback and respond, alsoin a timely manner.

In fact, the ideas of proactive feedback and usability improvement are key. Companies must provide proactive feedback to relevant stakeholders and help solve problems such as security vulnerabilitiesand policy and regulationvacuums.

Keeping AI "under meaningful human control"in the Chinese AI ethics policy will no doubt draw comparisons to Isaac Asimov's Three Laws of Robotics.The bigger question is whether China, the United States,and the European Union can find commonality on AI ethics.

Without question, the application of AI is increasing. In my opinionthe United States still holds the lead, with China closing the gap and the EUfalling behind. This increasing use is driving many towardthe idea of developing an international, perhaps evenglobal, governance framework for AI.

When you compare the principles outlined by China and those of the European High-level Expert Group on AI, many aspects align. But the modus operandi is very different.

Let's consider the concept of privacy. The European approach to privacy, as illustrated by the General Data Protection Regulation (GDPR), protects an individual's data from commercial and state entities. In China, personal data is also protected,but, in alignment with the Confucian virtueof filial piety, only fromcommercial entities, not from the state. It is generally accepted by the Chinese people that the state has access to their data.

This issue alonemay be something that prevents a worldwide AI ethics framework from ever being fully developing. But it will be interesting to watch how this idea evolves.

The rest is here:

What you need to know about China's AI ethics rules - TechBeacon