Category Archives: Ai

During one of the many live collaboration panels of MUSCs 2022 Innovation Week, an interesting discussion ensued, mirroring a common debate in health care and that is: How does artificial intelligence (AI) fit in?

Last week, as several clinicians and key members of the Clemson-MUSC AI Hub which was formed in 2021 were on hand at the Gazes Cardiac Research Institute, it became quickly evident that AI is gaining traction throughout the world of heath care. But equally evident was the fact that theres still some skepticism from the mainstream when it comes to the best ways to use it.

For congenital cardiologist G. Hamilton Baker, M.D., associate professor of pediatrics, AI remains a tremendous untapped resource.

AI is such a blanket term, he said in an interview right after the formation of the Clemson-MUSC AI Hub last year. Were leveraging data science and wrangling those giant databases with appropriately applied machine learning methods.

Baker has been utilizing AI in his work for several years now, working on a number of different AI+Biomedical projects ranging from congenital heart disease to diabetic eye disease.

I feel very strongly about education on AI. The goal is to teach clinicians how to understand and utilize AI. We arent asking people to learn how to code, we simply want them to learn how AI can work for them, Baker said.

At the Gazes, the topic quickly centered on AI and bias. Some clinicians believe the most elegant aspect of AI is that it removes unintended biases by letting the computers which are inherently without bias because theyre metal and silicone do the data crunching and leaving the treatment to the physicians.

When two clinicians might disagree on something, AI can help uncover unknown biases and dispel others, said MUSC Public Health Sciences assistant professor Paul Heider, Ph.D. AI just looks at the data and makes decisions that are based on that alone.

However, others argued that those AI programs were written by humans, and those inadvertent biases almost certainly were sprinkled in.

Trustworthiness is a key word that we need to be focusing on here, said Brian Dean, Ph.D., chairman of the Division of Computer Science at Clemson University. Because the AI system is becoming less of a smart sensor that provides input to the medical decision-making process and more of a teammate. So we have to be super careful because, after all, AI was trained based on human expert opinion, which is biased.

Dean agreed that AI is an extremely valuable tool for the medical field, cautioning all to simply be judicious with its use.

Jihad Obeid, M.D., co-director of the Biomedical Informatics Center at MUSC, agreed. If you use it as a decision aid, rather than a decision-maker, he said, AI can be a real asset.

Regardless of the differences of opinion in the room, panel members agreed that AI has unlimited potential for researchers and clinicians alike.

When it comes to AI in health care, its so tempting to talk about the hype, all the big stuff it can do, Baker said. But the truth of the matter is there are plenty of easy, smart projects where AI could really make a significant difference, and we just need more people on board.

According to MUSC provost Lisa K. Saladin, PT, Ph.D., MUSC is already using AI to develop techniques that can help to diagnose and treat a range of ills, including cancer, Alzheimers disease, substance abuse, child abuse, epilepsy, aphasia, inflammatory skin conditions and cardiac issues.

Baker said that clinicians who are interested in implementing AI into their research or practice should look into the AI Hub, as it offers a host of resources, including funding for AI. During this years Innovation Week, the Clemson-MUSC AI Hub gave out $100,000 worth of grants to five worthy projects.

We want people to know about this, he said. I know there are lots of people out there who could really use our help. We want to accelerate the adoption of AI for those who are interested."

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Can Artificial Intelligence remove unintended bias from health care? Clinicians optimistic, but wary - Medical University of South Carolina

Adam Oliner, co-founder and CEO of Graft used to run machine learning at Slack, where he helped build the companys internal artificial intelligence infrastructure. Slack lacked the resources of a company like Meta or Google, but it still had tons of data to sift through and it was his job to build something on a smaller scale to help put AI to work on the dataset.

With a small team, he could only build what he called a miniature solution in comparison to the web scale counterparts. After he and his team built it, however, he realized that it was broadly applicable and could help other smaller organizations tap into AI and machine learning without huge resources.

We built a sort of mini Graft at Slack for driving semantic search and recommendations throughout the product. And it was hugely effective And that was when we said, this is so useful, and so powerful if we can get this into the hands of most organizations, we think we could really change the way people interact with their data and interact with AI, Oliner told me.

Last year he decided to leave Slack and go out on his own and started Graft to solve the problem for many companies. He says the beauty of the solution is that it provides everything you need to get started. Its not a slice of a solution or one that requires plug-ins to complete. He says it works for companies right out of the box.

The point of Graft is to make the AI of the 1% accessible to the 99%. he said. What he means by that is giving smaller companies the ability to access and put to use modern AI, and in particular pre-trained models for certain specific tasks, something he says offers a tremendous advantage.

These are sometimes called trunk models or foundation models, a term that a group at Stanford is trying to coin. These are essentially very large pre-trained models that encode a lot of semantic and structural knowledge about a domain of data. And this is useful because you dont have to start from scratch on every new problem, he said.

The company is still a work in progress, working with beta customers to refine the solution, but expects to launch a product later this year. For now they have a team of 11 people, and Oliner says that its never too early to think about building a diverse team.

When he decided to start the company, the first person he sought out was Maria Kazandjieva, former head of engineering at Netflix. I have been working at building the rest of the founding team and also hiring others with an eye toward diversity and inclusion. So, you know, just [the other day], we were talking with recruiting communities that are focused on women and people of color, partly because we feel like investments now in building diverse team will just make it so much easier later on, he said.

As the journey begins for Graft, the company announced what it is calling a pre-seed investment of $4.5 million led by GV with help from NEA, Essence VC, Formulate Ventures and SV Angel.

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Slacks former head of machine learning wants to put AI in reach of every company - TechCrunch

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

Qulacomm said it is launching new platform support for advanced robotics products, 5G wireless networking and edge-AI applications.

The company unveiled the Qualcomm Robotics solutions at its annual Qualcomm 5G Summit event as it recognizes the ways in which 5G is proliferating beyond smartphones.

The San Diego, California-based company took the wraps off its Qualcomm Robotics RB6 Platform and the Qualcomm RB5AMR reference design, which can be used to build advanced edge-AI and robotics products using Qualcomms chips.

The solutions will help unlock new commercial markets for autonomous mobile robots, delivery robots, highly automated manufacturing robots, collaborative robots, unmanned aircraft, industrial drone infrastructure, autonomous defense solutions and beyond.

The platform combines Qualcomm AI Engine and 5G chip capabilities for cutting-edge applications and possibilities, including smarter and safer robots and environments.

The tech can be used to enable autonomy for delivery robots roaming the streets; fleet management coordination across autonomous mobile robots in industrial spaces; real-time data and insights that empower critical decision-making across manufacturing and logistics and intelligence to support autonomous urban air mobility transportation.

The Qualcomm Robotics RB6 Platform and the Qualcomm RB5 AMR reference design will support evolving applications for robot manufacturers that are looking to integrate ground robots in industrial use cases across sectors. This could include government service applications, logistics, healthcare, retail, warehousing, agriculture, construction, utilities and more.

The new solutions will accelerate the digital transformation of industries and serve as a key enabler for what Qualcomm calls Industry 4.0.

Building on the successful growth and traction of Qualcomm Technologies leading robotics solutions, our expanded roadmap of solutions will help bring enhanced AI and 5G technologies to support smarter, safer, and more advanced innovations across robotics, drones and intelligent machines, said Dev Singh, head of autonomous robotics, drones and intelligent machines at Qualcomm Technologies, in a statement. We are fueling robotics innovations with 5G connectivity and premium edge-AI that will transform how we think and approach challenges and ever-evolving industry expectations in the digital economy.

The Qualcomm Robotics RB6 platform is aimed at taking enterprise and industrial robotics innovation to the next level with enhanced AI and5G. The new solution delivers 5G connectivity with support for global sub-6GHz and millimeter-wave bands in the mainstream, enterprise, and private networks.

Weve been experiencing a lot of massive growth and traction in robotics space, said Singh, in a press event. What we have is another proof point on how we continue to expand our portfolio while delivering the solution that addresses the pain point of the ecosystem.

He said diversifying beyond smartphones to robotics is part of a general diversification strategy for Qualcomm.

Qualcomm is becoming a de facto choice for robotics applications, Singh said. Our technology is at [the] heart of all and serves as the brain of all cutting-edge robotics deployments and trends across industries.

Qualcomm is working with Adlink, Akasha Imaging (an Alphabet company), Cyngn, ForwardX, FutureMind, Hyundai Robotics, inVia Robotics, LG Electronics, Microsoft Azure private MEC, ModalAI, Naver Labs, PuduRobotics, Samsung Electronics, Teledyne Flir, Thundercomm, and more.

The Qualcomm RB5 AMR Reference Design is available for pre-sale now through ModalAI and the Qualcomm Robotics RB6 development kit is available for sale through Thundercomm.

As more and more robotics are being rolled out, we see that the robotics applications evolve into new areas, like the delivery robot for first mile and last mile, the urban air mobility, smart manufacturing, and also wireless factories, Singh said. Advancements in all of these areas are going to be significantly aiding humanity and generating a lot of economic output as well. And its going to make our lives better and simpler by simplifying logistics, transportation, manufacturing, and others.

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Qualcomm goes beyond smartphones with 5G and edge-AI robotics solutions - VentureBeat

The AI Collaborative brings together Leading Health System executives in an experiential community around the efficient, responsible, and future-focused deployment of artificial intelligence and machine learning.

BURLINGTON, Mass., May 11, 2022 /PRNewswire/ -- The growth of artificial intelligence (AI) and machine learning (ML) in healthcare points to a future in which these technologies will consistently aid clinical decision-making, workforce support and planning, patient experience, and overall business strategy. With the goal of creating a novel initiative around market forces and breakthrough innovations for AI and ML, The Health Management Academy (The Academy) has partnered with Nuance, a Microsoft company, to launch The AI Collaborative, which brings together senior leaders from the nation's Leading Health Systems (LHS) who are deploying these technologies in the clinical and operational settings, as well as planning for future use-cases.

Having joined forces this year, Microsoft and Nuance represent two of the most trusted and innovative technology organizations in the world. With decades of experience bringing cutting-edge cloud and AI technology to the healthcare, retail, financial services and telecommunications industries, Nuance and Microsoft are ideal partners in the effort to create a progressive peer community anchored in collaboration with key LHS executives and experts across the healthcare ecosystem.

The potential for AI in healthcare is clear across LHS in The Academy network, many of whom have already begun investing in AI-powered solutions. This investment has elevated the need for regular touchpoints with peers across the nation to share questions, successes, and challenges in real time. The AI Collaborative will commence in September 2022 and will include a visit to Microsoft's corporate headquarters and annual summits in subsequent years. These events will include key stakeholders across LHS leadership teams embedded in analytics, digital strategy, and clinical transformation, along with subject-matter experts from the med-tech, payer, and life sciences sectors.

Learning opportunities at The AI Collaborative Summits will anchor around stakeholders who are deeply committed to embracing AI as a transformative and disruptive tool in healthcare. Via workshops, learning exercises, and instant insights generation, The AI Collaborative Summits will empower attendees with tactical strategies to utilize patient-specific data and insights to augment care delivery, reduce care variation, and support operational improvements.

"Our members have expressed their desire for a dedicated space to explore AI in healthcare and its enormous potential to improve outcomes and clinical workflow," said Renee DeSilva, CEO of The Academy. "We are thrilled to expand our partnership with Microsoft and Nuance to introduce The AI Collaborative, a new program at The Academy designed exclusively for clinical and operational executives who lead their organization's approach to investing in AI as a strategic initiative."

Nuance is a technology pioneer with market leadership in conversational AI and ambient intelligence, and a full-service partner of 77 percent of U.S. hospitals and trusted by over 500,000 physicians daily. Microsoft provides trusted and secure cloud and AI capabilities with the goal to empower people and organizations to address the complex challenges facing the healthcare industry today.

With a long-term commitment to leveraging cloud and AI technologies to enhance patient engagement and outcomes, reduce clinician burnout, improve clinical quality and safety, and enhance financial performance, Nuance and Microsoft are leaders in the future-focused healthcare ecosystem and well-equipped to ensure The AI Collaborative members are at the front-end of education and learning on the evolution of AI in healthcare.

"The key to successful healthcare innovation using AI is understanding at a deep level the problems that you're trying to solve and focusing on the outcomes you want to achieve," said Peter Durlach, Chief Strategy Officer of Nuance. "With the combined engineering, market and domain expertise of Nuance and Microsoft, The AI Collaborative can bring together multiple technical, business and clinical stakeholders to prioritize deployment of solutions for clinician burnout, patient engagement and health system financial stability, while accelerating innovation in precision medicine, drug discovery, clinical decision support and other promising use cases across the entire healthcare ecosystem."

About The Health Management AcademyAt The Academy, we power our community to drive health forward. Our community is made up of healthcare's most influential changemakers including executives from the top 150 U.S. health systems and the most innovative industry partners. We power our members by building our community and fostering connections through executive peer learning. We support professional growth through talent and development. We accelerate understanding by delivering timely and actionable data and insights on key challenges. And we catalyze transformation by building alliances in areas where the power of the collective is greater than the power of one. Learn more at hmacademy.com.

About Nuance CommunicationsNuance Communications is a technology pioneer with market leadership in conversational AI and ambient intelligence. A full-service partner trusted by 77 percent of U.S. hospitals and 85 percent of the Fortune 100 companies worldwide, Nuance creates intuitive solutions that amplify people's ability to help others. Nuance is a Microsoft company.

ContactFor The Health Management AcademyGregory KantorContent and Communications Manager[emailprotected]

For NuanceDayna McCoubrey[emailprotected]

SOURCE Nuance Communications, Inc.

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The Academy and Nuance, a Microsoft Company, Partner to Launch The AI Collaborative - PR Newswire

For many litigators, legal research remains one of the most tedious aspects of practicing law. From not knowing whether youve exhausted your search to consuming large chunks of time, research can be draining in terms of time and financesmany clients just arent willing to pay hefty legal research bills.

Sluggish legal research continues to weigh lawyers down, despite the fact that we live in the age of Google and Alexa. If technology gives us the ability to have any question answered in seconds, why cant we find the case law we need just as easily?

While this has been the case for decades, theres been an evolutionary shift in the last few years due to an innovative few leveraging modern AI to make legal practice more efficient. Advanced AI has been used to bring forth what many see as a technological revolution in practice of law.

The modern AI behind new, intelligent legal tech includes machine learning (a branch of AI) and other sophisticated technologies. Rather than delve into the mechanics here, its more useful for litigators to know how this technology drives efficiency in a law practice, so they can stop fearing AI-powered legal tech and instead reap its benefits.

AI-powered search engines allow lawyers to conduct concept-based searches of legal databases, instead of trying to find the perfect keywords or Boolean operators. This gives lawyers the power to research quickly and intuitively by searching how they think. Lawyers can research faster and find cases they wouldnt have found using traditional search platforms.

This concept-based searching also applies to searching a law firms brief bank. This means lawyers can search a concept and find a brief theyve already written with similar issues and arguments, so they dont have to reinvent the wheel.

Certain litigation tasks can be automated through AI, including brief-drafting. Smart tools can even help lawyers write the first draft of their brief in less than 30 minutes.

Concept-based searching comes into play here, too. Searching by concepts allows lawyers to find exactly what they are looking for in millions of documents. Basically, AI actually finds the needle in the haystack, and does so instantly.

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Fear AI? Embrace it instead and watch your law firm thrive - JD Supra

TigerGraph, Inc.

Thousands of Attendees Expected at Global Celebration of the Power of Graph and AI; Virtual Event Set for May 24-26

REDWOOD CITY, Calif., May 11, 2022 (GLOBE NEWSWIRE) -- TigerGraph, provider of a leading graph analytics platform, today announced the complete agenda for Graph + AI Summit 2022, the industrys only open conference focused on accelerating analytics, AI and machine learning with graph algorithms. The roster includes confirmed speakers from the worlds most innovative organizations, including US Bank, Xbox, Intuit, Constellation Research, Gartner, and many more. The virtual conference, set for May 24-26, will feature keynotes from industry visionaries as well as breakout sessions, use cases, and panels led by data, analytics, and AI professionals.

The mission for Graph + AI Summit has always been to organize an open industry event to democratize and accelerate analytics, AI, and machine learning with graph algorithms. It is back, bringing together thought leaders, technical experts, and business innovators from the worlds largest and most successful organizations to share their graph-related experiences, best practices, ideas, and more, said Dr. Yu Xu, founder and CEO of TigerGraph. We will also announce and highlight the winning projects from the Graph for All Million Dollar Challenge at the summit. The challenge showcases how critical graph and AI are to addressing global issues, such as healthcare, the economy, and climate change. The submissions are astounding and were ready to give away one million dollars and share these ideas with the world!

Past Graph + AI Summits have attracted thousands of global graph enthusiasts from 70+ countries, all united in their commitment to using, advancing, and innovating with graph technology. Data scientists, data engineers, architects, and business and IT executives from over 180 of the Fortune 500 companies participated in the last event alone. Past speakers from Amazon, Capgemini, Gartner, Google, Microsoft, UnitedHealth Group, JPMorgan Chase, Mastercard, NewDay, Intuit, Jaguar Land Rover, Pinterest, Stanford University, Forrester Research, Accenture, KPMG, Intel, Dell, and Xilinx along with many others have shared how their organizations reaped the benefits of graph. Watch all 2021 sessions on demand here.

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Graph + AI Summit 2022 includes keynote presentations, executive roundtables, technical breakout sessions, industry tracks, and live workshops for advanced analytics and machine learning.

Keynote speakers presenting during conference general sessions include:

Jesse Janosov, general manager, Xbox Community at Microsoft, will discuss graph in gaming.

Afraz Jaffri, research director at Gartner, will present understanding when graph analytics and AI are best for your business.

Ray Wang, founder, chairman and principal analyst at Constellation Research, will deliver a keynote on the case for digital velocity.

Alaina Percival, founder and CEO of Women Who Code, will discuss diversity in data science and technology.

Notable roundtables and panel sessions include:

Connecting Data and Adapting to an Uncertain World: Speakers from Collibra, Databricks, and Kubrick will share the skills and knowledge needed to adapt to the challenges we face today.

How Are Business Leaders Implementing Digital Transformation?: Chris Preimesberger, editor for ZDNet and VentureBeat, will moderate a panel with executives from Capgemini, InfoSys, and Morrisons about how executives can achieve their strategic goals and embed digital in their business operations DNA to stay competitive.

Graph in Cybersecurity: Jonathan Swartz, senior reporter at MarketWatch, will moderate a panel with executives from Splunk, Data Connectors, and IBM about how graph analytics can help Chief Information Security Officers do more to understand and root out undetected advanced persistent threats.

Impact of AI on the Retail Industry: Executives from Capgemini, Google Cloud, and Reltio will discuss the challenges and opportunities retailers face today and the new technologies driving their success.

Defi, NFT, Metaverse - Where Do We Go From Here?: Speakers from Capgemini, Tempo Storm Esports, Rethink Ledgers, and Tokeny will examine graph-related topics, including taxonomy, digital asset creation, and blockchain, with examples derived from financial services, eSports, healthcare, and other industries during this dynamic, interactive panel discussion.

Additional sessions include speakers from AWS, Google, HPE, IBM, Intuit, Microsoft, US Bank, Verizon, and many more. The full agenda can be found here.

For more information on Graph + AI Summit or to register, please visit https://www.tigergraph.com/graphaisummit/

Graph for All Million Dollar Challenge Winners to be Announced at Graph + AI Summit

Last week, TigerGraph announced the finalists of the Graph for All Million Dollar Challenge, a global search for innovative ways to harness the power of graph technology and machine learning to solve real-world problems. The challenge brought together brilliant minds to build innovative solutions to better our future by answering one question: How will you change the world with graph? The challenge gained major traction worldwide with over 1,500 registrations from 100+ countries. Participants represent a wide range of backgrounds including data scientists, developers of all kinds full stack, front-end, back-end, mobile, product managers, designers, data engineers, machine learning engineers, and students. TigerGraph narrowed nearly 150 submissions down to the most innovative, game-changing, and applicable solutions. TigerGraph will reveal and feature all 15 winners of the challenge at the Graph + AI Summit 2022 event. For more information on the challenge and to see all finalists, please visit https://www.tigergraph.com/graph-for-all/.

Helpful Links

About TigerGraph TigerGraph is a platform for advanced analytics and machine learning on connected data. Based on the industrys first and only distributed native graph database, TigerGraphs proven technology supports advanced analytics and machine learning applications such as fraud detection, anti-money laundering (AML), entity resolution, customer 360, recommendations, knowledge graph, cybersecurity, supply chain, IoT, and network analysis. The company is headquartered in Redwood City, California, USA. Start free with tigergraph.com/cloud.

Media Contacts:

North AmericaTanya CarlssonOffleash PRtanya@offleashpr.com+1 (707) 529-6139

EMEAAnne HardingThe Message Machineanne@themessagemachine.com +44 7887 682943

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Graph + AI Summit 2022 Unveils Agenda Featuring Sessions and Speakers from US Bank, Xbox, Intuit, Constellation Research, Gartner, and More - Yahoo...

May 11 - The notion that modern technologies can help resolve some of the largest sustainability challenges has a long track record, not least among tech-minded brands. Many solutions just require a tweak of existing applications, like Googles decision to add methane analysers to cars that were already collecting data for its street view service, for example. Others are more targeted. Think, the super-successful Global Forest Watch app, a free deforestation tracking service.

Enthusiasm for so-called "tech-for-good" solutions shows no signs of abating, as shown by the continued flood of brand-led competitions, accelerators and hackathons. Typical is the XPRIZE, set up by Tesla chief executive Elon Musks charitable foundation, which recently named 15 finalists for an $80 million payout. The prize money, due to be dispersed in 2025, will go to the venture with the most compelling solution for removing carbon from the atmosphere.

Similar in ethos (if not in budget) is the new innovation connections initiative from Tesco, the UKs biggest supermarket. Designed to accelerate the growth of eco-minded startups in the food sector, the scheme sees eight early-stage firms compete to have their solution rolled out in the UK supermarkets supply chain.

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All the finalists have a strong tech profile, from the inventor of a bio-acoustics system for monitoring on-farm pest levels through to a fish-feed producer that manufacturers its product from waste-based microalgae.

The Global Cement and Concrete Association also picked out six promising startups this month for its Innovandi Open Challenge, all of which boast ground-breaking technologies geared towards achieving net zero.

The tech-for-good lens is being increasingly turned to solving sustainability-related management challenges within brands own operations. As the scope of social and environmental issues grows, so does the complexity of managing them. A sharp growth in consumer and investor scrutiny also means brands are more under the spotlight than ever to get it right. Claiming to have a relevant sustainability policy is no longer enough; brands need to provide evidence of a marked improvement in practices and performance.

Step forward the bots. According to recent research by U.S. tech giant Oracle, more than 93% of business leaders would trust artificial intelligence more than a human to make a sustainability decision. Underlying the finding is an assumption that bots make fewer mistakes when collecting data (43%), show less bias (42%), and predict future outcomes with greater accuracy (41%).

Digital technologies, especially artificial intelligence and machine learning, remain a fairly new space for many business practitioners, says Elena Avesani, global sustainability director at Oracle. But the potential for their application in the field of sustainability management is considerable.

She cites UK electricity and gas utility National Grid, which now uses cloud-based machine-learning models to calculate the volume of renewable electricity in the grid at any one time. The solution, which analyses data at a speed and breadth that would be impossible for humans to achieve, has increased the accuracy of the companys estimations by 40%, according to Avesani.

The pressure is mounting to really change the way you run your businesses. You need to be able to make strategic decisions that look at ESG (environment, social, and governance) issues as part of the mix of variables that are part of running your business. (This) is bringing innovation on multiple levels, she states.

The initial phase of management-focused tech applications centres around delivering efficiency and data-quality gains. Before brands consider providing evidence of improved performance externally, they need to obtain a clear and accurate picture of where they currently stand on multiple different issues, from employee diversity to greenhouse gas emissions. Smart software systems that can collate, organise, store and assess sustainability statistics from multiple data points make this task of internal stocktaking quicker and more reliable.

Once a clear baseline is established, sustainability practitioners have a working platform to set priorities, determine a strategic direction and chart progress. Given the relatively small size of most corporate sustainability departments, much of this analytical legwork is habitually outsourced to the growing crop of sustainability service providers and consultants.

A pioneer in the emerging software-as-a-service (SaaS) field for sustainability is Manifest Climate. Co-founded by environmental lawyer Laura Zizzo, the Toronto-based tech startup uses a machine-learning model to inform companies the extent to which their climate policies and governance systems are aligned with the 11 recommendations of the Taskforce on Climate-related Financial Disclosures (TCFD).

With Canada expected to follow the UK in making TCFD-aligned financial reporting mandatory, clients include the Canadian financial services firm Scotiabank, insurance company Manulife and mining company Teck Resources.

Zizzo says the ultimate purpose of a SaaS model is to free up management time to make more informed strategic decisions. We organise this information (about climate risk and disclosure) in a way that they can better understand so that they can then prioritise climate in a way that makes more sense. This central claim saw Manifest Climate recently win $30 million Canadian dollars ($23.98 million) in a successful funding round.

The next frontier for digital management solutions lies outside a brands own operations, a key challenge given the wider context in which macro-sustainability issues such as water management, carbon emissions and human rights play out. One example of how digital technology is being adapted for sustainable management is the application of blockchain in corporate supply chains. Startups such as Circulor (in mining, plastic, and construction), Retraced (in fashion), and Peer Ledger (food) are using the distributed database technology to track products from source to sale, thus answering a growing public demand for product-based chains of custody.

The challenge here is that many suppliers, especially smaller companies, are often privately owned and thus not subject to similar disclosure requirements as listed companies. In response, ESG data providers are building ever more sophisticated software systems to determine credible estimates from dispersed public data sets.

At the heart of all management tech is data. As Jon Sykes, chairman of Carbon Intelligence, a London-based climate information provider, puts it: Data is at the root of impact. Data is at the root of change. Data is at the root of transition. Thats not because numbers in and of themselves carry weight, but rather because if you get them right, they can provide a stable launchpad for impactful action.

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Opinions expressed are those of the author. They do not reflect the views of Reuters News, which, under the Trust Principles, is committed to integrity, independence, and freedom from bias. Sustainable Business Review, a part of Reuters Professional, is owned by Thomson Reuters and operates independently of Reuters News.

Oliver Balch is an independent journalist and writer, specialising on businesss role in society. He has been a regular contributor to The Ethical Corporation since 2004. He also writes for a range of UK and international media. Oliver holds a PhD in Anthropology / Latin American Studies from Cambridge University.

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Brand Watch: Under the spotlight as never before, companies turn to AI to get their numbers right - Reuters

SANTA CLARA, Calif.--(BUSINESS WIRE)--Metas VP of Infrastructure Hardware, Alexis Black Bjorlin, will open the flagship AI Hardware Summit with a keynote, while her colleague Vikas Chandra, Metas Director of AI Research will open Edge AI Summit. Other notable keynotes include Microsoft Azures CTO, Mark Russinovich, plus Wells Fargos EVP of Model Risk, Agus Sudjianto; Synopsys President & COO, Sassine Ghazi; Cadences Executive Chairman, Lip-Bu Tan; and Siemens EVP, IC EDA, Joseph Sawicki, among many others

Machine learning and deep learning are fast becoming major line items on agendas in board rooms in every organization across the globe. The technology stack needed to support these workloads, and to execute them quickly, efficiently, and affordably, is fast developing in both the datacenter and in client systems at the edge.

In 2018, a new Silicon Valley event called the AI Hardware Summit launched to provide a platform to discuss innovations in hardware necessary for supporting machine learning both at the very large scale, and in small resource-constrained environments. The event attracted enormous interest from the semiconductor and systems sectors, welcomed Habana Labs into the industry in its inaugural year, and subsequently hosted Alphabet Inc.s Chairman and Turing Award Winner, John L. Hennessy, as a keynote speaker in 2019. Shortly after, the Edge AI Summit was launched to focus specifically on deploying machine learning in commercial use cases in client systems.

Hennessy said of the AI Hardware Summit: Its a great place where lots of people interested in AI Hardware are coming together and exchanging ideas, and together we make the technology better. Theres a synergistic effect at these summits which is really amazing and powers the entire industry.

Fast forward a few years of virtual shows and the events are back in-person with a fresh angle. An all-star cast of tech visionary speakers will address optimizing and accelerating machine learning hardware and software, focusing on the intersection between systems design and ML development. Developer workshops with HuggingFace are a new feature this year focused on helping bring new hardware innovation into leading enterprises.

The co-location of the two industry-leading summits combines the proposition to focus on building, optimizing and unifying software-defined ML platforms across the cloud-edge continuum. Attendees of the AI Hardware Summit can expect content spanning from hardware and infrastructure up to models/applications, whereas the Edge AI Summit has a much tighter focus on case studies of ML in enterprise.

This years audience will consist of machine learning practitioners and technology builders from various engineering disciplines, discussing topics such as systems-first ML, AI acceleration as a full-stack endeavour, software defined systems co-design, boosting developer efficiency, optimizing applications across diverse ML platforms and bringing state of the art production performance into the enterprise.

While the AI Hardware Summit has broadened its scope beyond focusing purely on hardware, there will still be plenty for hardware-focused attendees to explore. The event website, http://www.aihardwaresummit.com, gives accessible information on why a software-focused or hardware-focused attendee should register.

The Edge AI Summit features more end user use cases than any other event of its kind, and is a must attend for anyone moving ML workloads to the edge. The event website, http://www.edgeaisummit.com, gives more information.

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Tech Visionaries to Address Accelerating Machine Learning, Unifying AI Platforms and Taking Intelligence to the Edge, at the Fifth Annual AI Hardware...

This might sound like a hot take but its not: In 50 years, when historians look back on the crazy 2020s, they might point to advances in artificial intelligence as the most important long-term development of our time. We are building machines that can mimic human language, human creativity, and human thought. What will that mean for the future of work, morality, and economics? Bestselling author Steven Johnson joins the podcast to talk about the most exciting and scary ideas in artificial intelligence and an article he wrote for The New York Times Magazine about the frontier of AI. Part of their conversation is excerpted below.

Derek Thompson: What I wanna do in the next 30 minutes to an hour is offer people a tour of the horizon of AI and ask some big, hard questions about what is exciting about this horizon, what is scary about this horizon, and how we can move the frontier of this technology toward the less dystopian implications of it. And I wanna start off with a very specific example of AI and that is GPT-3. Steven, what is GPT-3 and why are you excited about it?

Steven Johnson: Well, GPT-3 is a kind of a subset of AI. Its a specific implementation of a category known as large language models and it also belongs to the family of neural nets and the family of deep learning. So those are a bunch of buzzwords right there that will be meaningful.

DT: Were gonna unpack those buzzwords in just a second.

SJ: Yeah. But it is basically a neural net that is modeled very vaguely on the structure of the human brain, but we should not take that kind of biological analogy too far. That is, it goes through a process thats called a training process, where it is shown a massive corpus of text, basically a kind of a curated version of the open worldwide web, Wikipedia, a body of digitized books, that are part of the public domain. And it basically ingests all of that information. And this training process is really kind of fascinating. We can get into the details of it, but basically it learns to associate connections between all the words in that body of text. And through that training process, it is able then, when you give it promptsinitially it was in the form of heres a sentence or heres a paragraph, continue writing in this mode for another paragraph or another five paragraphs.

And if you have a big enough corpus of text and a deep enough neural network, it turns out that computers over the last couple of years have gotten quite good at continuing human-authored text. And it was initially kind of a little bit of a parlor trick in that you would write a paragraph and earlier versions of this software would kind of continue on and you would look at it and youd be like, Yeah, that sounds vaguely like a human could have written it, but obviously it was also nonsensical in all these ways. And it wasnt particularly interesting. For most users, you see this technology and things like autocomplete when youre using Gmail and you write a sentence and it suggests, you know, a little word at the end, thats basically built on top.

DT: Oh, right. It was great to see you last And then Gmail suggests in light-gray font, night.

SJ: Its the same idea.

DT: It is using its understanding of millions and millions of emails already sent to predict the next word in the email that you are sending. And just to add a little bit of sort of 101 context to your first answer, neural nets, were not gonna get into the full definition here, but basically this is a set of algorithms that mimic a human brain, that learn to identify patterns or solve problems through repeated cycles of trial and error. Its a domain of AI that is very popular, shows a lot of promise, and is behind the large language models that you just talked about. One of these large language models thats very exciting is GPT-3. And the reason I think GPT-3 is so interesting is that its not just the sort of technology that can add the word night when you type in Gmail, It was great to see you last. It can go much further than that. It can summarize books, it can summarize papers, it can write entire essays in response to very complicated prompts. Can you give us some examples of some of the implications of GPT-3 that are most thrilling to you?

SJ: Yeah. So let me say one more thing about the structure of it, which I think is kind of fascinating. And I agree we dont wanna get too far down into the rabbit hole of how it actually works, but on some fundamental level, it is trained on this very elemental act of next-word prediction. And to me this is one of the things that I find kind of mind-blowing about it. I mean, theres a lot of complexity to whats going on in the neural net, but fundamentally the training process is, you know, ingest all the history of the web and Wikipedia. And then its given endlessly a series of training examples where its shown a real-world paragraph that some human has written and then one word is missing. And basically in the initial stages of the training process, the software is instructed like, Come up with the missing word. Come up with a statistically ranked list of the most likely words in this particular paragraph.

And in the initial pass, itll give you, you know, whatever, 30,000 words that might be that missing word. And itll be terrible at it. Itll be awful. But somewhere at the bottom of the list, like word no. 29,000 will be the right word. And so the training process is saying, OK, whatever set of neural connections led you to make guess no. 29,000, strengthen all of those connections and weaken all of the other connections in your neural net. And it just plays that game a trillion times. And eventually it gets incredibly good at predicting the next wordand, in fact, predicting whole sentences or paragraphs. What seems to have happened over the last three or four yearsthere was an earlier version of GPT-3, which was called GPT-2, that came out a couple years ago. Over this period, the software is now much better, as you say, at constructing larger thoughts and making arguments and summarizing and doing things like that. So to me, its just mind-blowing that it really fundamentally comes out of this act of next-word prediction, that thats the kind of fundamental unit of the whole exercise.

This excerpt was lightly edited for clarity.

Host: Derek ThompsonGuest: Steven JohnsonProducer: Devon Manze

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The Future of AI Is Thrilling, Terrifying, Confusing, and Fascinating - The Ringer

It may sound like a sci-fi plot, but an artificial intelligence algorithm recently played a major part in the downfall of an elected government.

In January 2021, the government of the Netherlands decided to step down. The scandal thatset their resignation in motion began in 2012, with the introduction of a self-learning algorithm to seek signs of benefits fraud. The tax authority believed that the algorithm would flag up potential fraud cases, which humans could then assess to weed out any errors.

What played out, as IEEE Spectrum reports, was that the algorithm began to develop patterns of bias, disproportionately labeling those from lower-income backgrounds, immigrants, and ethnic minorities as having committed fraud. The humans tasked with vetting the cases perhaps too trusting of the algorithm accepted many of the false flags, and demanded that money be returned.

The consequences of the new system (and the humans in government who trusted it) cannot be overstated. Tens of thousands of people were left in poverty, while some died by suicide following giant tax bills which sometimes went into six figures.

WhenChermaine Leysner received a bill totaling100,000, she believed that the government had made a mistake in asking her to pay back the child allowance she had received since 2008.

I was working like crazy so I could still do something for my children like give them some nice things to eat or buy candy," Leysner told Politico. "But I had times that my little boy had to go to school with a hole in his shoe."

Following the stressfrom the bill, and other personal circumstances, she separated from herchildren's father. She was far from alone.

TheMinistry of Justice and Security Statistics commissioned an approximation of the number of children who had been placed in care following a false fraud flag. They found that more than 1,100 children had been separated from their families,placed in care, and kept from their families for years as a result of the scheme.

Around 70,000 children were affected by the bills, which were wrongly given to around 30,000 parents. The figure could be far higher, as the estimate was based on data after 2015.

Compensation of at least 30,000is to be paid to victims of the algorithm and the humans thatenabled its false assumptions, but it was not enough to save the government from the scandal, known in the country askinderopvangtoeslagaffaire.

"Innocent people have been criminalised and their lives ruined,"Prime Minister Mark Rutte told reporters as his government took the unanimous decision to step down in January as a result of the scandal."The buck stops here."

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The AI That Led To Children Being Re-homed And The Fall Of An Elected Government - IFLScience