Artificial Intelligence is still a nascent technology; much of the groundbreaking work moving the industry forward is done inside AI research labs. It's often from those labs that open source projects are started. Institutes like Open AI, NASA's JPL, Google Deepmind, MIT CSAIL, BAIR, The Turing Institute, and Max Planck -- to name just a handful -- are presenting at ODSC in 2019, helping us bring our community to the leading edge of AI. To learn more about the labs' role at ODSC, visit ODSC West. Since our next conference is in San Francisco, we're looking west at a few exciting research labs in the area that are participating in ODSC this year.

Emerging technologies help the public sector to enhance cities by protecting data and making the information safe and secure from the cyber attacks. FREMONT, CA: Technology helps shape the future in many ways. Along with these, the government is also changing. Governments are now trying to offer secure and safe tools to make the citizens "smart." The technologies employed by the government these days create a platform for more personalized and secure interactions and automate lower ordered tasks so the citizens can rely more on different kinds of jobs.

Google says it's investigating ways to preserve users' privacy without impacting their display ads experiences, in part through AI and machine learning. In a blog post this morning, the Mountain View company announced it'll soon introduce an ad frequency feature in Display & Video 360 -- its end-to-end programmatic ad campaign management platform -- that'll tap AI to help advertisers "[respect] user privacy" when third-party cookies aren't present. Google explains that the tool, which it plans to bring to display offerings in Google Ads in the near future, will leverage traffic patterns where a third-party cookie is available and analyze them at an aggregated level across Google Ad Manager publishers to generate predictive models. This will enable it to estimate how likely it is that users visit different publishers serving the same ads through Google Ad Manager, and to optimize how often those ads should be shown to users who lack third-party cookies. Google is already using machine learning in Google Ads, albeit mostly to generate ad suggestions, better match users' searches, and adjust video bids.

The divide between haves and have nots in experimenting and achieving results with machine learning is growing wider. The haves are clearly the technology companies. Facebook, Twitter, Salesforce and others shared significant details on what the of problems they were solving with machine learning and their efforts to standardize and scale their machine learning practices. Technology suppliers also demonstrated their latest capabilities and enterprise offerings with Intel AI, Microsoft Azure, and IBM Watson leading the charge. I reported last year that deep learning was more accessible to mainstream enterprises and at this year's conference, small and large vendors offered a mix of data science platforms, dataops frameworks, and data management tools to help enterprises start and mature experiments in machine learning.

Big data center operators say they are seeing a steady stream of new architectures for accelerating deep learning neural networks--and the flow is just getting started, according to comments at last week's AI Hardware Summit. One analyst pegged the number of established and startup companies designing AI accelerators at a whopping 130. "The machine-learning revolution has reopened the opportunity for new architectures…let a thousand flowers bloom," said Alphabet Chairman and former Stanford President John Hennessy in an opening keynote at the event. Such domain-specific chips don't have to be compatible with legacy object code so the industry "can introduce new architectures faster than in general-purpose computing," he added. Potential users from Alibaba, Facebook, Google, and Uber said the chip vendors need to show their benchmark scores, make their software easy to use, and conform to emerging standards. "We are sampling a few vendors' upcoming products, and one issue is using their software correctly…it takes a long time to vet hardware and a lot of time to bring new software into our ecosystem," said Linjie Xu [[CQ]], director of applied AI architecture at Alibaba Cloud, speaking on a panel.

Methane is a potent greenhouse gas that is being added to the atmosphere through both natural processes and human activities, such as energy production and agriculture. To predict the impacts of human emissions, researchers need a complete picture of the atmosphere's methane cycle. They need to know the size of the inputs--both natural and human--as well as the outputs. They also need to know how long methane resides in the atmosphere. For more information see the IDTechEx report on Smart City Opportunities: Infrastructure, Systems, Materials 2019-2029.

Sharing a similar viewpoint with these legends, Kevin Kimber, a well-known financial expert in business circles, also believes that AI can specifically power financial applications to streamline processes, connect data between finance and back-office operations with the front office and commercial function and in doing so increase a company's revenue. Case in point--Rimilia, a Fintech solutions provider with Kevin as its CEO, is a perfect example for intelligent cash application, credit management, and collections in the finance industry. Today, the modern corporation has unique opportunities to automate in many areas that previously weren't possible and with the maturity of a small number of super-intelligent applications and their development within the finance industry, now is the time for companies to take advantage and drive change. Current day finance technology providers need to not only understand the complexities that run across the financial estate but also assist companies in carefully navigating forward. With an in-depth knowledge of these complexities and professional expertise to successfully get around them, Kevin Kimber, CEO of Rimilia, knows the opportunities companies have.

Waymo, the self-driving division of Alphabet, is about to put more passengers its fully driverless Chrysler Pacifica minivans. The company emailed its customers in the suburbs of Phoenix, Arizona, to let them know that "completely driverless Waymo cars are on the way." It's a sign that Waymo is growing confident enough in its technology to increase the frequency at which it allows passengers to ride in autonomous vehicles without a safety driver behind the wheel. The email, which was published on Reddit and confirmed as authentic by a spokesperson, was sent to members of Waymo's early rider program, a 400-plus cadre of suburban Arizonans who signed nondisclosure agreements with Waymo to test its self-driving cars. Waymo also operates an invite-only commercial ride-hailing service called Waymo One that includes around 1,000 people.

"But until we have a hint of a beginning of a design, with some visible path towards autonomous AI systems with non-trivial intelligence, we are arguing about the sex of angels." This time it's the big one: will AI rise up and murder us all? While this isn't a new topic – humans have postulated about AI overlords for centuries – the timing and people involved in this debate make it interesting. Don't miss Hard Fork Summit in Amsterdam We're absolutely in the AI era now, and these dangers are no longer fictional. The architects of intelligence working on AI today could, potentially, be the ones who cause (or protect us from) an actual robot apocalypse.

Nielsen (NLSN) and the University of Oxford today announced a two-year collaboration to advance the use of artificial intelligence (AI) to identify and classify consumer packaged goods (CPG) products on shelves in retail stores. Facilitated between Nielsen's Image Recognition group and the Visual Geometry Group (VGG) at the University of Oxford, this partnership brings together the world's largest pool of product reference data with industry-leading brainpower around AI technology to yield greater accuracy in product identification and discovery. Through this partnership, Nielsen is working directly with University of Oxford Professors Andrew Zisserman and Andrea Vedaldi (Department of Engineering Science), world-renowned computer scientists and pioneers in image recognition and AI research. Zisserman, Vedaldi and their team of research scientists will work together with Nielsen to more precisely and quickly identify and classify in-store products based on product images captured through Nielsen's eCollection solution. The Oxford researchers will focus on building and enhancing the eCollection algorithms with increasingly advanced deep learning capabilities, enabling a more automatic detection of store products, promotions and prices without the need for manual intervention.