Collaborating Authors


Precision, Accuracy, Scale – And Experience – All Matter With AI


When it comes to building any platform, the hardware is the easiest part and, for many of us, the fun part. But more than anything else, particularly at the beginning of any data processing revolution, it is experience that matters most. Whether to gain it or buy it. With AI being such a hot commodity, many companies that want to figure out how to weave machine learning into their applications are going to have to buy their experience first and cultivate expertise later. This realization is what caused Christopher Ré, an associate professor of computer science at Stanford University and a member of its Stanford AI Lab, Kunle Olukotun, a professor of electrical engineer at Stanford, and Rodrigo Liang, a chip designer who worked at Hewlett-Packard, Sun Microsystems, and Oracle, to co-found SambaNova Systems, one of a handful of AI startups trying to sell complete platforms to customers looking to add AI to their application mix. The company has raised an enormous $1.1 billion in four rounds of venture funding since its founding in 2017, and counts Google Ventures, Intel Capital, BlackRock, Walden International, SoftBank, and others as backers as it attempts to commercialize its DataScale platform and, more importantly, its Dataflow subscription service, which rolls it all up and puts a monthly fee on the stack and the expertise to help use it. SambaNova's customers have been pretty quiet, but Lawrence Livermore National Laboratory and Argonne National Laboratory have installed DataScale platforms and are figuring out how to integrate its AI capabilities into the simulation and modeling applications. Timothy Prickett Morgan: I know we have talked many times before during the rise of the "Niagara" T series of many-threaded Sparc processors, and I had to remind myself of that because I am a dataflow engine, not a storage device, after writing so many stories over more than three decades. I thought it was time to have a chat about what SambaNova is seeing out there in the market, but I didn't immediately make the connection that it was you.

Intellectual property and investment in Artificial Intelligence


Patents provide third-party opinions on the uniqueness of the technology and a'saleable asset insurance' in the event that the company ceases trading

(Artificial Intelligence) OR #AI_2022-06-29_21-27-28.xlsx


The graph represents a network of 5,014 Twitter users whose tweets in the requested range contained "(Artificial Intelligence) OR #AI", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 30 June 2022 at 04:43 UTC. The requested start date was Thursday, 30 June 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 1-hour, 30-minute period from Monday, 27 June 2022 at 22:30 UTC to Thursday, 30 June 2022 at 00:01 UTC.

TikTok tells senators how it plans to beef up data security for American users


In a letter to nine Republican senators, TikTok said it's working to "remove any doubt about the security of US user data." CEO Shou Zi Chew reiterated a claim that TikTok stores American user data on servers run by Oracle, which will be audited by a third party. Chew also said the company expects to "delete US users' protected data from our own systems and fully pivot to Oracle cloud servers located in the US." "[We] are working with Oracle on new, advanced data security controls that we hope to finalize in the near future," Chew wrote in the letter, which was obtained by The New York Times. "That work puts us closer to the day when we will be able to pivot toward a novel and industry-leading system for protecting the data of our users in the United States, with robust, independent oversight to ensure compliance." Chew was responding to questions in a letter sent by the Republican senators -- including Roger Wicker, the ranking Republican member of the Senate Commerce Committee -- following a report by BuzzFeed News.

Senior Machine Learning Engineer


The Machine Learning (ML) team supports production-facing experiences and solutions including search engines, style and template recommendations, dynamic content generation, phishing detection, personalized user experiences and more. You will be a Senior ML Product engineer reporting to the manager of Machine Learning. ML Product builds production systems and services to bring machine learning to engineering teams across Squarespace. Squarespace has access to incredibly rich sources of user content and behavior, and our team is focused on leveraging those unique datasets to provide every customer with the experience they need to be successful. You will engage with stakeholders, refine requirements, rapidly prototype, deploy and maintain production machine learning systems.

SEO in Real Life: Harnessing Visual Search for Optimization Opportunities


The most exciting thing about visual search is that it's becoming a highly accessible way for users to interpret the real world, in real time, as they see it. Rather than being a passive observer, camera phones are now a primary resource for knowledge and understanding in daily life. Users are searching with their own, unique photos to discover content. Though SEOs have little control over which photos people take, we can optimize our brand presentation to ensure we are easily discoverable by visual search tools. By prioritizing the presence of high impact visual search elements and coordinating online SEO with offline branding, businesses of all sizes can see results.

10 new AI unicorns flying high in 2022


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. Technology venture capital deals may be slowing down, but investment in artificial intelligence (AI) companies continues to boom. Investments in AI research and applications are set to hit $500 billion by 2024, according to research firm IDC, while PwC predicts AI will contribute $15.7 trillion to the global economy by 2030. So, it's no surprise that among the 206 new 2022 "unicorns" – that is, privately held startups with a value of over $1 billion – a boatload are in artificial intelligence and machine learning. Join us at the leading event on applied AI for enterprise business and technology decision makers in-person July 19 and virtually from July 20-28.

Vectra AI wins the "Excellence in Threat Solutions Award" at the SC Media Awards Europe 2022 - Actu IA


The London Marriott Hotel Grosvenor Square was the venue for the SC Media Awards 2022, the cybersecurity industry's coveted and prestigious awards ceremony on June 21. Vectra, a leader in AI-based cyber threat detection and response for hybrid and multi-cloud enterprises, won the "Excellence in Threat Solutions Award" in the "Best Enterprise Behavioral Analysis and Threat Detection" category for its Vectra AI platform. Vectra didn't just win that title, however, as it was also ranked at the event as "Highly Commended" in the "Best Use of Machine Learning and Artificial Intelligence", "Best Customer Service" and "Best Security Company" categories. Founded in 2010 and based in San Jose, California, Vectra is a leader in threat detection and response for hybrid and multi-cloud enterprises. Its Vectra AI platform uses AI to quickly detect threats in the public cloud, identity, SaaS applications and data centers.

The History of Artificial Intelligence - Science in the News


It began with the "heartless" Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can't machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.

Becoming an 'AI Powerhouse' Means Going All In


There are plenty of organizations that are dabbling with AI, but relatively few have decided to go all in on the technology. One that is decidedly on that path is Mastercard. Employing a combination of acquisitions and internal capabilities, Mastercard has the clear objective of becoming an AI powerhouse. Just what does that term mean, and how is it being applied at the company? Some refer to the idea of aggressive, pervasive adoption of AI as being "AI first." Others use the term "AI fueled" or "all in on AI" (that's Tom's favorite, since it's the title of his forthcoming book on the subject).