Retail giant Amazon has pioneered the idea of automated shopping, as seen with its Amazon Go store format. The first of these launched in January 2018 in downtown Seattle and nearly 30 others have opened since. The concept is now catching on with other companies – including Tesco, the UK’s biggest supermarket and third-largest retailer in the world measured by gross revenues. It has just launched its own automated store in central London.
The rollout of this technology at Tesco Express High Holborn follows a successful trial in Welwyn Garden City, a town north of London. The High Holborn branch has already been a cashless store since it first opened in 2018 and is now checkout-less too.
The newly developed system – called “GetGo” – offers the same products but with a faster and more convenient shopping experience. A customer simply downloads the mobile app, scans the QR code generated on their screen, picks up the groceries they need and then leaves the store.
SambaNova Systems, a company that builds advanced software, hardware, and services to run AI applications, announced the addition of the Generative Pre-trained Transformer (GPT) language model to its Dataflow-as-a-Service™ offering. This will enable greater enterprise adoption of AI, allowing organizations to launch their customized language model in much less time — less than one month, compared to nine months or a year.
“Customers face many challenges with implementing large language models, including the complexity and cost,” said R “Ray” Wang, founder and principal analyst of Constellation Research. “Leading companies seek to make AI more accessible by bringing unique large language model capabilities and automating out the need for expertise in ML models and infrastructure.”
Cybereason, a Tel Aviv-and Boston, Massachusetts-based cybersecurity company providing endpoint prevention, detection, and response, has secured a $50 million investment from Google Cloud, VentureBeat has learned. It extends the series F round that Cybereason announced in July from $275 million to $325 million, making Cybereason one of the best-funded startups in the cybersecurity industry with over $713 million in the capital.
We reached out to a Google Cloud spokesperson, but they didn’t respond by press time.
The infusion of cash comes after Cybereason and Google Cloud entered into a strategic partnership to bring to market a platform — Cybereason XDR, powered by Chronicle — that can ingest and analyze “petabyte-scale” telemetry from endpoints, networks, containers, apps, profiles, and cloud infrastructure. Combining technology from Cybereason, Google Cloud, and Chronicle, the platform scans more than 23 trillion security-related events per week and applies AI to help reveal, mitigate, and predict cyberattacks correlated across devices, users, apps, and cloud deployments.
The nation excels in computer vision and facial recognition, but practical applications are limited to surveillance. The U.S. has much broader expertise.
Creating human-like AI is about more than mimicking human behavior — technology must also be able to process information, or ‘think’, like humans too if it is to be fully relied upon. New research, published in the journal Patterns and led by the University of Glasgow’s School of Psychology…
Magnetic solids can be demagnetized quickly with a short laser pulse, and there are already so-called HAMR (Heat Assisted Magnetic Recording) memories on the market that function according to this principle. However, the microscopic mechanisms of ultrafast demagnetization remain unclear. Now, a team at HZB has developed a new method at BESSY II to quantify one of these mechanisms and they have applied it to the rare-earth element Gadolinium, whose magnetic properties are caused by electrons on both the 4f and the 5d shells. This study completes a series of experiments done by the team on nickel and iron-nickel alloys. Understanding these mechanisms is useful for developing ultrafast data storage devices.
In 2,021 Instagram will be the most popular social media platform. Recent statistics show that the platform now boasts over 1 billion monthly active users. With this many eyes on their content, influencers can reap great rewards through sponsored posts if they have a large enough following with this many eyes on their content. The question for today then becomes: How do we effectively grow our Instagram account in the age of algorithmic bias? Instagram expert and AI growth specialist Faisal Shafique help us answer this question utilizing his experience growing his @fact account to about 8M followers while also helping major, edgy brands like Fashion Nova to over 20M.
Using machine learning, a computer model can teach itself to smell in just a few minutes. When it does, researchers have found, it builds a neural network that closely mimics the olfactory circuits that animal brains use to process odors.
Animals from fruit flies to humans all use essentially the same strategy to process olfactory information in the brain. But neuroscientists who trained an artificial neural network to take on a simple odor classification task were surprised to see it replicate biology’s strategy so faithfully.
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When asked to classify odors, artificial neural networks adopt a structure that closely resembles that of the brain’s olfactory circuitry.
If the properties of materials can be reliably predicted, then the process of developing new products for a huge range of industries can be streamlined and accelerated. In a study published in Advanced Intelligent Systems, researchers from The University of Tokyo Institute of Industrial Science used core-loss spectroscopy to determine the properties of organic molecules using machine learning.
The spectroscopy techniques energy loss near-edge structure (ELNES) and X-ray near-edge structure (XANES) are used to determine information about the electrons, and through that the atoms, in materials. They have high sensitivity and high resolution and have been used to investigate a range of materials from electronic devices to drug delivery systems.
However, connecting spectral data to the properties of a material—things like optical properties, electron conductivity, density, and stability—remains ambiguous. Machine learning (ML) approaches have been used to extract information for large complex sets of data. Such approaches use artificial neural networks, which are based on how our brains work, to constantly learn to solve problems. Although the group previously used ELNES/XANES spectra and ML to find out information about materials, what they found did not relate to the properties of the material itself. Therefore, the information could not be easily translated into developments.
While the pandemic is still raging, the chaos of the past 18 months has calmed a bit, and the dust is starting to settle. Now the time has come for healthcare CIOs and other health IT leaders to look forward and plan their IT investments – shaped, in no small part, by the lessons of the recent past.
1:42 Are we on the wrong train to AGI? 4:20 Marvin Minsky and AI generalization problem. 11:57 Defining intelligence in AI 17:17 Is AI masquerading as a trendy statistical analysis tool? 23:35 AI systems lack our most basic intuitions. 27:38 The public not wanting to face Reality. 29:36 Equipping AI with Kant’s categories of the mind (Time, Space, Causality) 33:40 Neural nets VS traditional tools. 34:50 Causality in AI 37:14 Lack of interdisciplinary learning. 45:54 How can we achieve human level of understanding in AI? 49:21 More limitations. 59:35 Motivation in inanimate systems. 1:01:31 Lack of body and transcendent consciousness. 1:05:55 What interdisciplinary learning would you encourage? 1:06:49 Book recommendations.
Gary Marcus is CEO and Founder of Robust AI, well-known machine learning scientist and entrepreneur, author, and Professor Emeritus at New York State University.
Dr. Marcus attended Hampshire College, where he designed his own major, cognitive science, working on human reasoning. He continued on to graduate school at Massachusetts Institute of Technology, where his advisor was the experimental psychologist Steven Pinker. He received his Ph.D. in 1993.
His books include The Algebraic Mind: Integrating Connectionism and Cognitive Science, The Birth of the Mind: How a Tiny Number of Genes Creates the Complexities of Human Thought, Kluge: The Haphazard Construction of the Human Mind, a New York Times Editors’ Choice, and Guitar Zero, which appeared on the New York Times Bestseller list. He edited The Norton Psychology Reader, and was co-editor with Jeremy Freeman of The Future of the Brain: Essays by the World’s Leading Neuroscientist, which included Nobel Laureates May-Britt Moser and Edvard Moser. Together with Ernie Davis, he authored Rebooting AI and is well known to deconstruct myths of the AI community.
In 2,014 he founded Geometric Intelligence, a machine learning company. It was acquired by Uber in 2016. In 2,019 he founded Robust AI and acts currently as Robust AI’s CEO.
