Category Archives: Ai

Last week, two very exciting things happened. First, I boarded a plane for Barcelona to connect with so many customers and partners in person after such a long gap, which felt completely surreal. Secondly, I presented a new demo to a keen crowd of highly influential tech strategy shapers, industry analysts, and media.

Whether it was finally attending a physical event, meeting some of our partners for the first time in real life, the thrill of unveiling a new partnership, wearing a face mask while continuously talking for 12 hours straight, or a heady cocktail of all the above: something hit me. When you work in tech, you spend so much time looking forward to the future. But walking onto our showroom floor, I realized something about the now.

In the decade leading up to 2030, widespread commercial use of AI and 5G will redefine business and fast-track economic growth. Weve been talking about the potential of combining these technologies for a long time. By converging AI and 5G, literally thousands of enterprises (with a helping hand from application developers) will be able to rewrite their workplace rulebooks. Last week at MWC, I realized were one step closer. The first domino has fallen.

In this blog post, Ill share some of the exciting use cases were currently seeing and announce the latest brainchild of the Cloud RAN team at Ericsson and our partner NVIDIA, which we developed with help from our ecosystem collaborators HPE and Chooch.

If 5G provides an infinite smorgasbord of delicious innovations generating tons of rich data, AI is the head chef explaining how to eat it. Up to 100 times faster than 4G, 5G delivers ultra-low latency, greater bandwidth and ultra-reliable, highly secure connectivity creating a world of limitless connectivity and limitless opportunities. On the other hand, AI applications on 5G networks can unlock intelligence to drive innovation and decision-making and deliver superior experiences.

AI adoption in business is skyrocketing and according to PwC, it will generate a whopping USD 15.7 trillion for the global economy by 2030. Applied at the network edge, AI enables new use cases such as autonomous guided vehicles, video analytics, asset tracking, robotic factories and more. Throw Cloud RAN into the mix, and youve got yourself a real treat (but more on that later).

But getting it right is a collaboration game. Communication service providers, telecom vendors, device manufacturers, AI application developers, cloud infrastructure providers, and hardware vendors need to innovate together across the full stack of components needed for 5G (and beyond) to deliver on the promise to transform enterprise. And this powerful new ecosystem is already creating win-win-wins for everyone involved.

Ericsson and NVIDIA, inventor of the Graphics Processing Unit (GPU) and leading AI provider, are partnering in the AI on 5G space to explore flexible, efficient, and secure AI enterprise applications over 5G networks.

One of the key use cases for this concept and what our demo last week was based on is intelligent video analytics. Intelligent video analytics allows businesses to leverage video feed data to make powerful business decisions and optimize processes. AI-powered video software detects and identifies different objects in a video and classifies them to enable intelligent video analysis, such as search, filtering, alerts, and data aggregation and visualization.

Examples of whats possible with intelligent video analytics include:

Video analytics solutions have seen significant growth lately thanks to their ability to provide invaluable business insights, and its no surprise. The possibilities that this technology unlocks for enterprises are endless, and together, I'm excited to say that Ericsson and NVIDIA are ready to help our communication service providers (CSPs) and their enterprise customers to explore them.

The Ericsson-NVIDIA concept we presented at MWC delivers AI applications at the edge of a high-performance 5G Cloud RAN, allowing for data to be processed on-premise to provide real-time decisions and alerts. Running AI and 5G on the same Cloud infrastructure lowers total cost of ownership and pre-integration makes it much easier for enterprises to adopt AI on 5G solutions.

NVIDIAs AI-on-5G Platform opens a new technical playbook by delivering AI applications at the edge over a high-performance, software-defined 5G RAN. Its a homogenous scale-out platform (a rack of 1RU telecom-grade servers running both AI and 5G workloads) that is easily expandable from small to large deployments. Thanks to its modular architecture of AI, 5G, compute and orchestration/management stacks, it can support different customer configurations too.

NVIDIA also brings an entire suite of AI-enabled applications and partners to the table, creating many more opportunities for joint innovation (yes, Im back to looking forward now). But were not alone. This new end-to-end solution is bolstered by a much bigger, exciting ecosystem.

Hewlett Packard Enterprise (HPE) is collaborating with us to deliver RAN-optimized telecom servers that help service providers reduce complexity and accelerate innovation for 5G deployments at scale. Leading enterprise AI platform provider Chooch is also working closely with us to develop and deploy more world-class computer vision solutions.

Together, we can also easily expand the solution to enable other use cases, for example, drone traffic analytics, quality assurance detection, AI-powered autonomous stores, and remote connected vehicles.

For enterprises, running AI applications at the edge on a high-performance 5G RAN is essential for more efficient, intelligent operations. For service providers, deploying AI applications at the 5G edge creates new revenue sources: positioning AI to be one of the prime applications for 5G.

Judging by the nodding heads in the audience, it was clear that the potential of AI on 5G use cases struck a chord with my audience at MWC. But the main question from the audience was how far can this technology go? What other use cases are potentially possible?

My answer: there are many more on the horizon. Were excited about developing this concept further and continue to explore the potential of this technology with NVIDIA and our wide ecosystem of partners. I believe the universe of 5G and AI use cases is vast and colorful, and just like our own, the further we look, the more amazed we will be.

Join the conversation on how AI and 5G can bolster enterprise digitalization with Ericsson and NVIDIA.

Ericsson Cloud RAN passes GSMAs NESAS security audit

The four key components of Cloud RAN

What role will 5G and AI have in the mobile networks of the future?

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AI on 5G use cases for innovation-hungry businesses - Ericsson

PALO ALTO, Calif.--(BUSINESS WIRE)--In the fourth paragraph, second sentence of release dated March 15, 2022, should read: Toshinori Kujiraoka's (instead of Toshinori Kujuriraoka's).

The updated release reads:

JAPANS COMMITMENT TO AI SPURS FURTHER INTERNATIONAL EXPANSION BY SAMBANOVA SYSTEMS TO MEET THE COUNTRYS TECHNOLOGY PRIORITIES

AI Technology Leader Appoints Toshinori Kujiraoka as Country Sales Director of SambaNova Systems Japan, GK

SambaNova Systems, the company delivering the industrys only comprehensive software, hardware, and solutions platform to run AI and Deep Learning applications, announces further international expansion with the appointment of Toshinori Kujiraoka as country sales director for Japan. The appointment signals SambaNovas launch into the Japanese market and comes as Japans public and private sectors are heavily investing in AI.

The Japanese government set its AI strategy in 2019 and declared it would create the World's Most Advanced Digital Nation with an AI-ready infrastructure. The budget for 2022 includes a large investment in AI research and human resource development so the time is now for SambaNova to establish a presence in the region, said Toshinori Kujiraoka, Country Sales Director of SambaNova Systems Japan, GK. SambaNova is entering the Japanese market at the perfect time as our integrated AI software and hardware platform can directly support commercial enterprises and the Japanese government's AI strategy.

With more than 30 years of international sales experience, Toshinori Kujiraoka has been involved in numerous national projects at NEC, Sun Microsystems, Arm, ITS (Ministry of Land, Infrastructure, Transport and Tourism), Cyber Force (National Police Agency, Defense Agency), Human Genome Analysis (University of Tokyo), Fugaku Supercomputer, and the Next Generation Data Center Project. Toshinori Kujiraoka has spearheaded numerous market launches and contributed to sales totaling several hundred billion yen. Toshinori Kujiraoka will oversee business and sales development, operations and strategy within the region.

"AI is ushering in a massive change. We're excited to grow our presence in Japan to contribute to the countrys mission to be at the forefront of AI," said Rodrigo Liang, co-founder and CEO, SambaNova Systems. "Toshinori Kujiraoka's vast experience and strong track record of delivering customer success in the Japanese market makes him the right leader to scale our operations to support Japans AI strategy."

About SambaNovas Dataflow-as-a-Service

SambaNovas flagship offering, Dataflow-as-a-Service, is an extensible AI services platform, and enables organizations to jump-start AI initiatives overnight by augmenting existing capabilities and staffing with a simple subscription. The platform is powered by DataScale, an integrated software and hardware platform delivering unrivaled performance, accuracy, scale and ease of use built on SambaNovas Systems Reconfigurable Dataflow Architecture.

With AI becoming a business necessity in the global economy, customers need complete solutions that can run at scale in a financially viable way. With an integrated full-stack system, including best-in-class AI models, software and hardware, SambaNova provides the most expansive, accessible and impactful AI applications in the world.

About SambaNova Systems

AI is here. With SambaNova, customers are deploying the power of AI and deep learning in weeks rather than years to meet the demands of the AI-enabled world. SambaNovas flagship offering, Dataflow-as-a-Service, is a complete solution purpose-built for AI and deep learning that overcomes the limitations of legacy technology to power the large and complex models that enable customers to discover new opportunities, unlock new revenue and boost operational efficiency. Headquartered in Palo Alto, California, SambaNova Systems was founded in 2017 by industry luminaries, and hardware and software design experts from Sun/Oracle and Stanford University. Investors include SoftBank Vision Fund 2, funds and accounts managed by BlackRock, Intel Capital, GV, Walden International, Temasek, GIC, Redline Capital, Atlantic Bridge Ventures, Celesta, and several others. For more information, please visit us at sambanova.ai or contact us at info@sambanova.ai. Follow SambaNova Systems on LinkedIn.

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CORRECTING and REPLACING Japan's Commitment to AI Spurs Further International Expansion by SambaNova Systems to Meet the Country's Technology...

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Volvo Car USA has announced a new pilot program that will use artificial intelligence (AI) technology to thoroughly inspect the condition of tires, underbodies, and exterior panels and paint on used cars in a matter of seconds. The program will go through a trial phase on the U.S. East Coast, and is expected to expedite service calls and speed up vehicle trade-ins at the automakers service stations.

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An automated system can help resolve problems, Rick Bryant, vice-president for sales operations at Volvo Car USA. It shows the vehicles actual condition. The result is that customers will be able to see flaws such as a rusty tailpipe that they didnt know about. And theyll also know the retailer is upfront with them.

Developed in partnership with Israel-based UVeye, the technology uses machine learning and AI technology to combine three separate systems to quickly offer a comprehensive vehicle inspection.

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That companys Helios system is designed to scan for and detect frame damage, oil leaks, and more; its Artemis system ensures that all the wheels match, finds sidewall damage, measures tread depth, and more; and, lastly, its Atlas system provides a 360-degree scan of the exterior of the vehicle and detects dents, scratches, rust, and more.

With the ability to improve service wait times, create a digital health report, and assure Volvo customers of its safety standards, the company hopes that more than 280 of its dealers will eventually install the technology, if the pilot is successful.

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Volvo trialing AI that scans a car's condition in seconds - Driving

This quarterly update summarizes key federal legislative and regulatory developments in the first quarter of 2022 related to artificial intelligence (AI), the Internet of Things (IoT), connected and automated vehicles (CAVs), and data privacy, and highlights a few particularly notable developments in the States. In the first quarter of 2022, Congress and the Administration focused on required assessments and funding for AI, restrictions on targeted advertising using personal data collected from individuals and connected devices, creating rules to enhance CAV safety, and childrens privacy topics.

Artificial Intelligence

Members of Congress introduced legislation that would expand federal oversight over the use of AI in certain decision-making processes, as well as legislation that would increase resources for AI-related research and development. For example, this quarter, Senator Ron Wyden (D-OR) introduced the Algorithmic Accountability Act of 2022 (S. 3572), which would create a Federal Trade Commission (FTC) Bureau of Technology and would direct the FTC to promulgate regulations requiring covered entities to (1) perform impact assessments on deployments of any automated decision system (defined as any system, software, or process, including those derived from AI, the result of which serves as a basis for human judgment) used to make a critical decision (defined broadly as a decision or judgment that has any legal, material, or similarly significant effect on a consumers life related to the cost or availability of certain topics such as education, utilities and transportation, financial service, healthcare, or any other service that is established through rulemaking) and (2) submit summary reports of those impact assessments to the FTC. Only Democrats, however, have cosponsored the bill. In a closely divided Congress, it will remain difficult to move any legislation without bipartisan support. What that dynamic in mind, negotiations are likely to continue throughout this Congress in an attempt to reach a bipartisan agreement on the use of AI, particularly as it relates to advertisement targeting using algorithms.

Additionally, Congress has focused on efforts to increase AI funding. For example, the America COMPETES Act of 2022 (H.R. 4521), which passed the House this quarter, incorporates the AI-related provisions of several other bills introduced in the last year that aim to increase support for AI research. The House and the Senate are expected to conference on the COMPETES Act and the United States Innovation and Competition Act of 2021 (S. 1260), which will result in compromise legislation. These funding provisions are expected to be part of the final bill.

Internet of Things

Federal lawmakers have introduced legislation addressing the intersection between connected devices and targeted advertising. For example, Senator Cory Booker (D-NJ) and Representative Anna Eshoo (D-CA-18) introduced the Banning Surveillance Advertising Act of 2022 (S. 3520; H.R. 6416) this quarter, which would prohibit advertising facilitators (defined as a person that receives monetary consideration or any other thing of value to disseminate an advertisement, and collects or processes personal information to disseminate an advertisement) from using personal data to target advertisements to individuals or a connected device associated with the individual. The bill would provide the FTC with rulemaking authority, and the FTC would be empowered to enforce violations through its Section 5 authority.

Additionally, this quarter, federal regulators continued to engage with IoT-related policy across the federal government, particularly in the Federal Communications Commission (FCC) and the National Institute of Standards and Technology (NIST). For example, the FCC on January 10, 2022 announced a commitment of $361,037,156.16 million to support 802 schools and 49 libraries as part of its Emergency Connectivity Fund. The schools and libraries are approved to receive nearly 654,000 connected devices and more than 313,000 broadband connections. Relatedly, consistent with its obligations under Executive Order 14028 on Improving the Nations Cybersecurity, NIST published a whitepaper in coordination with the FTC and other agencies to initiate cybersecurity labeling pilot programs that will enable consumers to make informed decisions about IoT products. The whitepaper provides recommendations on consumer IoT product label criteria, label design and consumer education considerations, and conformity assessment considerations. Specifically, the whitepaper recommends coupling a binary label (a seal of approval type of label indicating a product has met a baseline standard) with additional information accessible online for interested consumers.

Connected and Autonomous Vehicles

The Department of Transportation (DOT) continued to engage on issues related to CAVs, particularly by (i) releasing a first-of-its-kind final rule amending the Federal Motor Vehicle Safety Standards (FMVSSs) to account for automated driving systems and (ii) seeking input on the projects and issues that should be considered by the Non-Traditional Emerging Transportation Technology (NETT) Council, a newly established entity under Section 25008 of the Infrastructure Investment and Jobs Act:

Federal lawmakers also engaged on CAV-related issues. The House Transportation and Infrastructure subcommittee held a hearing in early February, The Road Ahead for Automated Vehicles, in which experts, labor leaders, and industry representatives highlighted the need to increase consumer trust in CAVs and called for a national framework to facilitate the safe deployment of CAVs.

On February 28, 2022, the California Public Utilities Commission (CPUC) issued its first Drivered Deployment permits to Cruise LLC and Waymo LLC, allowing for passenger service in CAVs with a safety driver present. The CPUC uses the term drivered to refer to CAVs with safety drivers present, while those without safety drivers are referred to as driverless. For more information on this development, see this post from Inside Tech Media.

Data Privacy

Legislators and the Executive branch have expressed interest in childrens privacy this quarter. For example, President Bidens State of the Union address focused on childrens privacy online, specifically asking Congress to introduced legislation aimed at childrens privacy protections. On the Hill, The Kids Online Safety Act, (S. 3663) introduced by Senator Richard Blumenthal (D-CT) and co-sponsored by Senator Marsha Blackburn (R-TN), would create requirements for new safeguards, tools, and transparency requirements for minors online. Notably, the bill would create a duty to act in the best interests of a minor that uses the covered entitys (defined broadly as any commercial software or online application likely to be used by a minor) products or services. The bill would also require covered entities to conduct annual independent audits of the risk of harm to minors on their service and issue a public report based on its findings. For more information on this bill, see this post from Inside Privacy.

Additionally, the Utah legislature passed a comprehensive data privacy bill this quarter, which will go to the governor next for his signature. The bill provides consumers right access and deletion rights, as well as rights to opt-out of the sale of personal information, targeted advertising, and processing of sensitive data. If signed by the governor, the Attorney General will have authority to enforce the laws requirements. For more information on this bill, see this post from Inside Privacy.

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U.S. AI, IoT, CAV, and Privacy Legislative Update - First Quarter 2022 - Lexology

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Pony.ai develops autonomous robotaxi and trucking technology. | Source: Pony.ai

Pony.ai agreed to issue a recall on some versions of its autonomous driving software following a car accident that occurred in October 2021. The company recalled the now repaired vehicles because they used software associated with the crash.

During the October accident, a Pony.ai vehicle hit a street sign on a median during a turn in Fremont, California while in autonomous mode. No one was injured in the incident, but it prompted the California Department of Motor Vehicles (DMV) to suspend the companys driverless testing permit.

While there are plenty of crashes reported involving autonomous vehicles, this one stood out because the vehicle was operating in autonomous mode and didnt involve any other vehicles.

According to reporting from Reuters, Pony.ai said that the crash occurred less than 2.5 seconds after the autonomous driving system shut down. The National Highway Traffic Safety Administration (NHTSA) told the company that it believed the software had a safety defect, which lead to the requested recall.

Pony.ai complied with the request and updated the software in the three vehicles. According to the agency, this is the first recall of an autonomous driving system.

Pony.ai received its autonomous driving permit from the California DMV six months before it suspended it. At the time, Pony.ai was the eighth company to receive the permit, after Apollo, AutoX, Cruise, Nuro, Waymo, WeRide and Zoox.

Earlier this month, Cruise and Waymo received Drivered Deployment permits from the California Public Utilities Commission (CPUC). The permit allows the companies to charge customers fares for their services. The permits require both companies to have a safety driver present in the vehicles at all times.

This week, Pony.ai announced that it is now valued at $8.5 billion dollars after the first close of the companys Series D funding round. The company ended its last funding round at a $5.3 billion valuation.

Pony.ai plans to use the funding to further augment hiring and enter more strategic partnerships. It also plans to invest in research and development, including global testing of its fleet of robotaxis and robotrucking and moving closer to mass production and commercial deployment of its fleet.

The company did not disclose details on the amount raised in the funding round so far, but plans to announce totals when the full round closes.

See the rest here:

Pony.ai agrees to recall 3 of its autonomous vehicles - The Robot Report

By Xiaoman Hu

Encouraging women in AI has never been more urgent. A study by the World Economic Forum noted a gender disparity of 78 percent male versus 22 percent female in AI and data science. This disparity isnt just a challenge within the workforce. It reflects a highly nuanced issue that goes beyond any single workplace and if not addressed will have highly negative implications for society.

We have seen a lot of work to encourage girls and women to become interested in STEM and address gaps in digital skills at an earlier age than in the past. Yet now, there appears to be less effort to support women as they transition from higher education into a sustainable career in tech. This is a challenge for the industry. But the real problem is that as AI becomes ubiquitous in daily life, without a technology workforce that accurately reflects the structure of society, AI-based decisions are constrained by the limited societal and cultural biases of their designers. The impact of such homogeneity in AI decisions and bias has already been seen in examples such as the automation of credit card and mortgage applications, to resume screening and other areas.

The industry challenge is not due to a lack of skills. Research from the Turing Institute suggests women are trailing behind men with industry-relevant skills such as computer science, data preparation and exploration, general-purpose computing, databases, big data, machine learning, statistics, and mathematics. Yet much of this is not due to formal skills, but rather confidence by women in stating these abilities during recruitment and in the workplace. In the tech world where technical skills are needed, soft skills are sometimes dismissed but in order to move forward, there needs to be a greater focus on leadership and mentorship to build confidence and encourage a more diverse workforce. We say that stereotypes must be combatted from a young age yet a gap remains. For example, within the tech sector, women generally have higher levels of formal education than their male counterparts yet academic citations are fewer suggesting there is a lack of confidence in sharing academic knowledge. The Turing Institute finds that only 20 percent of UK data and AI researchers on Google Scholar are women. Of the 45 researchers with more than 10,000 citations, only five were women.

When I say that women need to have mentors and role models, I write from firsthand experience. It was only after winning a mathematics modeling competition in university that I considered a related career. This inspired me to write a blog on machine learning algorithms. The easy-to-understand method employed helped the blog garner over 5 million views, and eventually led to a career in programming. When I became a programmer and found myself working as the only woman in a room of men typically 10-15 years older, I struggled to relate and realized the need for a community of like-minded people.

In April 2020 I started to manage operations for MindSpore, an AI framework developed by Huawei, just as it became open source. MindSpore is Huaweis alternative AI framework to Googles TensorFlow and Facebooks PyTorch with comparable capabilities but 20 percetn fewer lines of code. Launched in September 2019, it is endorsed by major universities including Peking University, University of Edinburgh, and Imperial College. Today, MindSpore boasts over 1.3 million downloads and an interactive community indicated by over 19,000 issues, over 52,000 pull requests, and over 16,000 stars (the equivalent of a like among developers).

In 2021, open-source component downloads grew 73 percent YOY. With the rapid growth in the global adoption of open source technology, diversity in open source communities is also increasing. The MindSpore Women in Tech Community emphasizes seminar-like gatherings which provide women a safe space to discuss the challenges they face in the workplace. Mentoring is important. For example, in 2020, when the community was just in its infancy, a student at one of our events explained she was getting good grades but was worried about a career in programming. She sought advice from more senior programmers and tech leaders. By the time she graduated she had no need to worry and was able to choose from one of several offers. Not only did she feel more confident but was able to give back to the community by sharing her experience with new students, those who were now in the position she had been the previous year. It is experiences like this that will keep women in tech. When they stay, tech also benefits.

But encouraging women isnt simply about creating diversity within the industry to enable greater gender balance. The benefits stretch beyond the sector and into the societal benefits. With the digitalization of many traditional sectors, the pervasive nature of AI demands that it not only provides efficiency but is also inclusive. It is only by broadening the pool of talent that we can avoid data-led decisions skewed by bias. Establishing communities that actively foster participation and diverse voices is an important step.

Bias in AI starts with the initial formulation of problems. The questions are naturally constrained by the experiences of the designers and programmers. This in turn impacts the quality of the data and the way it is handled. So what will be the societal impact if there is not greater diversity?

So in conclusion, now that our lives are digitally-driven, we must ensure that women can enjoy the benefit of technology for generations to come rather than be negatively impacted.

About the author

Xiaoman Hu is the Director of Operations at MindSpore Community

Continued here:

Encouraging women in tech is essential to protect society against AI bias - TechTalks

Editor's note: This article is the latest in a series that looks into the ways supply chains, warehouses and manufacturing facilities are investing in technology. Here's the previous story.

Artificial intelligence isn't yet perfect.The technology has led to some major business flubs (like that time Microsoft's AI chatbot "learned" to be racist, sexist and anti-Semetic in 2016). But AI is not going away in the supply chain. In fact, it's prevalence is expected to grow.

In the 2021 MHI Annual Industry Report, 17% of respondents said they use AI already, and another 45% predicted they'll use it in five years. The survey of more than 1,000 supply chain professionals worldwide also found that 25% plan to make investments in AI products in the next three years.

"[AI] is very complex, but what we can use is very, very simple. People don't need to have a deep understanding of the algorithms," said Ben Lynch, director of business data analytics at DHL Supply Chain. "I never thought it would be able to move as quickly as it has, and it's only going to get better."

In the last five years, AI has shifted the relationship DHL has with its customers.

The company went from giving customers information for something that's already happened to "something that's a bit more predictive," Lynch said. "Through AI, machine learning and data availability, now we can give them insights into not just what's happened but what's going to happen."

That transition has been fueled by sophisticated algorithms that can handle the sheer amount of data collected.

"Every two years, we are generating as much data that has ever been created. By 2023, we'll have twice as much data in the world. Because of this, there's been a big need for technology to help support this data," Lynch said.

AI is also enabling the advancement of other kinds of technologies in the supply chain, like robotics, said Thomas Evans, robotics chief technology officer at Honeywell.

% of respondents who plan to invest in products and services over the next three years

"The complexity and the way-quicker access to AI through third party providers, and also the ability to build AI platforms and deploy them, is a drastic change and asset to supply chain logistics," Evans said. "It's only going to get more advanced as we harness more and more data."

AI isn't a panacea for business problems, though. It has experienced growing pains.

More recently than Microsoft's chatbot ordeal, the online real estate company Zillow shutdown Zillow Offers, an AI-fueled home buying and flipper service,because the company bought houses for higher than they could resell. The company took a $304 million inventory write down in the third quarter of this 2021.

But in the right use cases, AI is sophisticated, effective and already paying off.

A McKinsey report found that, for early adopters, AI-enabled supply chain management improved logistics costs by 15%, inventory levels by 35%, and service levels by 65%, compared to "slower-moving competitors."

The biggest gap Lynch sees to further adoption is simplifying data and "getting data and business to speak the same language," he said.

"As the amount of technology and digitalization increased and opened up more and more data, the struggle we've had is, how do you take this data and transform it into something that makes more sense for the operator?" he posed.

That will help drive decision-making on the operator end.

Lynch doesn't expect the flow of data to stop, either. He said he thinks it will grow along with the booming e-commerce landscape, fueled by all the consumer data drawn from that online activity.

"Now that we can understand what a consumer is going to buy next week, how do we set up our warehouses?" he said. "The technology get better and better and we'll get a really good sense at the consumer level as opposed to the aggregate supply chain level."

Wider roll out of AI will also depend on robust cybersecurity, Evans said. If data can't be captured, stored and shared with company partners securely, it becomes a liability instead of a boost.

"That's why we're seeing some of the vulnerabilities and ransomware concerns bigger businesses are having," he said, adding that, right now, he's working on a commercial product release and said that 80% to 90% of his engineers are focused on product security.

As the value of data to run AI systems become more valuable to businesses, they become valuable to bad actors, too. "Over time it becomes more of a target," Evans said.

This story was first published in our Operations Weekly newsletter. Sign up here.

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Here to stay: Supply chains gear up for investments in AI - Supply Chain Dive

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Virtually every enterprise decision-maker across the economic spectrum knows by now that artificial intelligence (AI) is the wave of the future. Yes, AI has its challenges and its ultimate contribution to the business model is still largely unknown, but at this point its not a matter of whether to deploy AI but how.

For most of the C-suite, even those running the IT side of the house, AI is still a mystery. The basic idea is simple enough software that can ingest data and make changes in response to that data but the details surrounding its components, implementation, integration and ultimate purpose are a bit more complicated. AI isnt merely a new generation of technology that can be provisioned and deployed to serve a specific function; it represents a fundamental change in the way we interact with the digital universe.

So even as the front office is saying yes to AI projects left and right, it wouldnt hurt to gain a more thorough understanding of the technology to ensure it is being employed productively.

One of the first things busy executives should do is gain a clear understanding of AI terms and the various development paths currently underway, says Mateusz Lach, AI and digital business consultant at Nexocode. After all, its difficult to push AI into the workplace if you dont understand the difference between AI, ML, DL and traditional software. At the same time, you should have a basic working knowledge of the various learning models being employed (reinforcement, supervised, model-based ), as well as ways AI is used (natural language processing, neural networking, predictive analysis, etc.)

With this foundation in hand, it becomes easier to see how the technology can be applied to specific operational challenges. And perhaps most importantly, understanding the role of data in the AI model, and how quality data is of prime importance, will go a long way toward making the right decisions as to where, when and how to employ AI.

It should also help to understand where the significant challenges lie in AI deployment, and what those challenges are. Tech consultant Neil Raden argues that the toughest going lies in the last mile of any given project, where AI must finally prove that it can solve problems and enhance value. This requires the development of effective means of measurement and calibration, preferably with the capability to place results in multiple contexts given that success can be defined in different ways by different groups. Fortunately, the more experience you gain with AI the more you will be able to automate these steps, and this should lessen many of the problems associated with the last mile.

Creating the actual AI models is best left to the line-of-business workers and data scientists who know what needs to be done and how to do it, but its still important for the higher ups to understand some of the key design principles and capabilities that differentiate successful models from failures. Andrew Clark, CTO at AI governance firm Monitaur, says models should be designed around three key principals:

As well, models should exhibit a number of other important qualities, such as reperformance (aka, consistency), interpretability (the ability to be understood by non-experts), and a high degree of deployment maturity, preferably using standard processes and governance rules.

Like any enterprise initiative, the executive view of AI should center on maximizing reward and minimizing risk. A recent article from PwC in the Harvard Business Review highlights some ways this can be done, starting with the creation of a set of ethical principles to act as a north star for AI development and utilization. Equally important is establishing clear lines of ownership over each project, as well as building a detailed review and approval process at multiple stages of the AI lifecycle. But executives should guard against letting these safeguards become stagnant, since both the economic conditions and regulatory requirements governing the use of AI will likely be highly dynamic for some time.

Above all, enterprise executives should strive for flexibility in their AI strategies. Like any business resource, AI must prove itself worthy of trust, which means it should not be released into the data environment until its performance can be assured and even then, never in a way that cannot be undone without painful consequences to the business model.

Yes, the pressure to push AI into production environments is strong and growing stronger, but wiser heads should know that the price of failure can be quite high, not just for the organization but individual careers as well.

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What business executives need to know about AI - VentureBeat

Signaling its ongoing commitment to collaboration in the areas of artificial intelligence and data science, the University of Florida is participating in an academic conference to address the potential of artificial intelligence, robotics and automation in agriculture.

The conference, titled Envisioning 2050 in the Southeast: AI-driven Innovations in Agriculture, is hosted March 9-11 by the Auburn University College of Agriculture and funded by the U.S. Department of Agriculture National Institute of Food and Agriculture.

Conference speakers include Hendrik Hamann, a distinguished research staff member and chief scientist for the future of climate in IBM Research; Mark Chaney, engineering manager of the automation delivery teams at Intelligent Solutions Group at John Deere; Steven Thomson, a national program leader with the USDA National Institute Food and Agriculture; and dozens more.

Speakers from academia, the federal government and industry will share their work in areas such as crop production, plant and animal breeding, climate, agricultural extension, pedagogy, food processing and supply chain, livestock management and more.

The Envisioning 2050 in the Southeast: AI-Driven Innovations in Agriculture conference will bring together academics, industry and stakeholders to share their expertise and develop a vision for the future, said Arthur Appel, interim associate dean of research for the Auburn College of Agriculture. Attendees will be able to learn about the depth and breadth of AI in agriculture from the experts who are making the promise of AI a reality.

Kati Migliaccio, co-organizer of the conference and chair of the Department of Agricultural and Biological Engineering at the University of Florida, said the timing of the conference is perfect.

This is an opportune time to host this conference focusing on AI in agriculture in the Southeast because of the resources invested in AI, the state of innovation of AI in agriculture and the critical need to adapt agriculture for current world challenges, including labor, nutrition, energy and climate, she said.

In November, the chief academic officers of the 14 member universities in the Southeastern Conference (SEC) announced formation of an artificial intelligence and data science consortium for workforce development, designed to grow opportunities in the fast-changing fields of AI and data science.

Believed to be the first athletics conference collaboration to have such a focus, the SEC Artificial Intelligence Consortium enables SEC universities to share educational resources, such as curricular materials, certificate and degree program structures, and online presentations of seminars and courses; promote faculty, staff, and student workshops and academic conferences such as todays event at Auburn; and seek joint partnerships with industry.

Joe Glover, provost and senior vice president for academic affairs at the University of Florida, which is leading the SEC-wide effort, said, AI is changing nearly every sector of society, and the SEC is uniquely positioned to engage students, faculty, and staff in one of the most transformational opportunities of our time. The combined strength of our institutions gives us the opportunity to advance in how we process the future of teaching and learning, research and economic development and how we can provide leadership at this critical moment when AI and data science are changing the way we think about small tasks and big questions.

The Auburn University office of communications and marketing and the SEC communications office contributed to this story.

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Universities meet to discuss future of AI and data science in agriculture - University of Florida

Theres no doubt: Machine learning and artificial intelligence are the hot specialties in IT right now but filling those jobs is proving to be tough.

In a September Gartner survey of over 400 global IT organizations, 64% of IT executives said that a lack of skilled talent was the biggest barrier to adoption of emerging technologies, compared with 4% the previous year.

Companies are looking for employees with specific training, skills and personal traits to fill positions STEM degrees, credentials specific to AI and machine learning, practical hands-on experience, and certain soft skills are all considered when deciding whether to hire a candidate.

The current push to find AI developers and engineers makes the shortage of candidates undeniable, and makes hiring particularly grueling.

In a November survey of over 2,500 human resources and engineering personnel by HackerEarth, a software company that helps organizations with their technical hiring needs, 30% of respondents said theyre expecting to hire more than 100 developers in the coming year.

With goals that ambitious, a significant portion of those hiring managers are so much in need for talent that theyre willing to compromise their standards. Nearly 35% of engineering managers said they would compromise on candidate quality to fill an opening quicker and nearly 24% of HR managers said the same.

According to the survey, AI and ML experts are in high demand this year, with demand exceeding supply.

What we have today is a rich tapestry of interrelated jobs or personas that all go into creating a data science or AI outcome in the enterprise.

Bradley Shimmin, chief analyst for AI platforms, analytics and data management, Omdia

Its a candidates market out there, said Vishwastam Shukla, chief technology officer for HackerEarth.

With companies from all industries looking to hire, larger organizations have the advantage of being able to offer bigger salaries and plusher benefits, he acknowledged. But hes seeing smaller employers and fast-growing startups put up a good fight for candidates.

One of the most popular tactics, Shukla said, is to actually inculcate a culture of learning and development within the organization.

The AI positions companies are looking to fill have become narrower and more specialized.

Job requirements vary wildly depending on a companys size, how mature they are, their data infrastructure and what kind of projects theyre working on, said Bradley Shimmin, chief analyst for AI platforms, analytics and data management at global analyst firm Omdia.

Five years ago, data scientist was considered the hottest job on the planet, and we were talking about data scientists as unicorns in that they possessed a number of very specific skills mathematical, statistical, business and communication, he said.

Companies realized early on that they couldnt operationalize with just a few jack-of-all-trades data scientists.

Trying to scale with them was impossible financially and that, coupled with the creation of MLOps platforms, really spawned a diversification for the job role and a slicing off of aspects of that job, said Shimmin. What we have today is a rich tapestry of interrelated jobs or personas that all go into creating a data science or AI outcome in the enterprise.

The job titles of AI and ML engineers and developers cover a wide variety of tasks and responsibilities, but theres a lot of overlap.

A necessary background for a potential employee starts with programming experience and a college degree.

Companies specifically look for:

And companies are hiring anywhere from basic entry-level positions to more advanced roles.

Were just hiring at all levels, said Valerie Junger, chief people officer at Quantcast, a technology company that focuses on AI-driven real-time advertising.

Machine learning engineers have to be fluent in Java, C++, Python, or similar development languages, and need anything from a masters degree to a Ph.D., depending on the role, she said.

Just having a general computer science degree isnt enough recruiters look for an applicant whos taken specific courses in AI and ML.

In the past, I would check that applicants had a math or STEM background only, said Rosaria Silipo, head of data science evangelism at KNIME, a data-analytics platform company. Now, with the proliferation of college programs and online courses, I check if they have any credentials specific to machine learning or data science.

The requirements for a machine learning engineer have changed, said Omdias Shimmin: All the platform players, Microsoft, Google, Amazon, and others are setting up certification programs.

You dont need to have a Ph.D. you can take whatever time it takes to prove certification as a machine learning engineer, or a data learning engineer, or as a machine learning specialist, and you can put that to work, he said. You can have a bachelors or a masters and still get into this area.

Pursuing specific credentials can lead to better jobs, or to a pay bump in a current position.

According to an October survey of over 3,000 data and AI professionals by learning company OReilly, 64% said they took part in training or obtained skills to build their professional skills, and 61% participated in training or earned certifications to get a salary increase or promotion.

And over a third of those polled dedicated more than 100 hours to training. Those survey participants reported an average salary increase of $11,000.

Entering competitions or hackathons can make a person stand out in a pool of prospective AI/ML candidates who have similar degrees and credentials.

For a candidate, entering hackathons helps potential employees connect with companies and learn a lot about how an organization works.

In the past, I would check that applicants had a math or STEM background only. Now, with the proliferation of college programs and online courses, I check if they have any credentials specific to machine learning or data science.

Rosaria Silipo, head of data science evangelism, KNIME

For an organization looking to hire a lot of people quickly, hackathons can provide a bounty of leads.

Hackathons let you create this warm pool of talent, because a lot of times when you actually go out to hire in the market, you may not be able to source the right kind of candidates with the right skill sets at a short notice, said HackerEarths Shukla.

For entry-level candidates, one of the most direct ways to learn how a company operates is through interning and a company can see if theyre a good fit.

We try to bring on interns who we can get to know before they graduate and they get to experience our culture beforehand, said David Karandish, founder and CEO at enterprise AI software-as-a-service company Capacity.

We really lead with Hey, heres the type of work youre going to do here. And we like people who are excited about the work that they do.

In the DevOps era, teams need to be increasingly cross-functional as businesses and data-driven product development come together. Good communication and collaboration skills are considered as important as a degree or a certification.

AI professionals need to explain complex topics often across multiple time zones, in a remote work setting, and be understood by a wide variety of people with various levels of technical knowledge.

No one person is ever going to know how every single thing works, noted Karandish. So organizations need people who can collaborate and coordinate together, and know when to ask for help or to bring up an important issue.

Its knowing when to ask, are we going down the right path or not, or is there a different approach?

And attitude goes hand-in-hand with collaboration.

Nobody wants to be working with a jerk, he said. They tend to not be collaborative and tend to take credit when credit isnt due. So wed like people with a high-talent-to-low-ego ratio in general.

A wide variety of companies are hiring, and an AI professional needs to understand the specific issues theyre trying to solve for their employer.

They need to have the proper domain knowledge to be able to provide precise recommendations and critically evaluate different work models, said Kamyar Shah, CEO at World Consulting Group.

To design self-running software for businesses and customers, they need to understand both the company and the issues their designs solve for that company, he said.

Problem-solving is another highly valued skill not just understanding what a problem is, but being able to come up with new solutions.

A big aspect of ML and AI is creating playbooks that have not been built before, said Wilson Pang, CTO of data company Appen. A developer needs to have the ability to try new techniques, test and learn, and continually grow through keeping up with industry trends.

Featured image by Alex Knight via Unsplash.

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