Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Kosinski, an assistant professor of organizational behavior, said he was studying links between facial features and political preferences, with preliminary results showing that AI is effective at guessing people's ideologies based on their faces. That means political leanings are possibly linked to genetics or developmental factors, which could result in detectable facial differences. Facial recognition may also be used to make inferences about IQ, said Kosinski, suggesting a future in which schools could use the results of facial scans when considering prospective students.

In March the company bought a startup cofounded by Geoffrey Hinton, a University of Toronto computer science professor who was part of the team that won the Merck contest. Extending deep learning into applications beyond speech and image recognition will require more conceptual and software breakthroughs, not to mention many more advances in processing power. Programmers would train a neural network to detect an object or phoneme by blitzing the network with digitized versions of images containing those objects or sound waves containing those phonemes. A team led by Stanford computer science professor Andrew Ng and Google Fellow Jeff Dean showed the system images from 10 million randomly selected YouTube videos.

Last week, the WannaCry ransomware attack crippled their network -- one report suggested people with life-threatening injuries were told not to come to the hospital. In the future, security systems could use artificial intelligence to monitor user behavior, track activity, suggest when there may be a danger and even mount an attack against the ransomware purveyors, effectively rendering the deadly malware client inoperable. Raja Mukerji, the cofounder and Chief Customer Officer at ExtraHop Networks, equates how an AI can block ransomware to how airport security stops people from using water bottles. A new technique using AI in airport security would not block all water bottles.

More overlooked machine learning and/or machine learning-related projects? OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. From Intel comes a(nother) deep learning framework, optimized for distribution over Apache Spark. BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

To help accelerate AI research, Pichai announced that the Tensor Processing Units (TPUs) it uses to train machine-learning models is available in the Google Cloud Platform for anyone to use via the Google Compute Engine today. "We want it to be possible for hundreds of thousands of developers to use machine learning," Pichai said. Our new Cloud TPUs accelerate a wide range of machine learning workloads, including training and inference https://t.co/aWvTVMn54Q The CEO also announced that Google will be using the neural nets it creates to build other neural nets with AutoML. The system takes a set of candidate neural nets (Pichai called them baby neural nets) and iterate them using a reinforcement training approach until the best one is found.

They can be used to perform a variety of different functions within the realms of customer service, customer interaction and many different "top of the funnel" advertising and marketing initiatives. While chatbots have been around for a while, they recently blew up in popularity when Facebook allowed branded chatbots onto its Facebook Messenger app. Instead of having to call and sit on a phone waiting to ask a five-second question, you can simply message a customer service chatbot and get your answer in a flash. Chatbots can play a large role analyzing customer data, and optimizing sales and marketing strategies in light of this analysis.

The human mind is no longer capable of keeping up with the velocity, volume, and variety of Big Data streaming through daily operations, making AI a powerful and essential tool for optimizing the analyzing and decision-making processes. Using all this information, it makes a data reservoir of relevant insights that may contain solutions to a wide range of critical issues, faced by IT operations and DevOps teams on a daily basis. The real-time obstacles DevOps engineers, IT Operations managers, CTOs, VP engineering, and CISO face numerous challenges, which can be mitigated effectively by integrating AI in log analysis and related operations. Quickly find the needle in the "IT operations" haystack and eliminate the main problems The good AI integration can yield Using AI driven log analytics systems, it becomes considerably easy to find the needle in the haystack, and efficiently solve issues.

All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA. Join Cloud Expo / @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 20th Cloud Expo / @ThingsExpo June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.

In other words, he hopes the new chip and the new service will set Google's cloud business apart from services offered by its main rivals, including Amazon and Microsoft, the unnamed competitive threat underlying his I/O keynote. Between its two AI labs--Google Brain, based at company headquarters in Silicon Valley, and DeepMind, a London AI startup Google purchased a little more than three years ago--Google is leading the new wave of artificial intelligence research and development so rapidly changing entire industries and economies. But the company believes cloud computing--where computing power is rented over the internet to businesses and software developers--could one day bring in far more. Google built its new chip as a better way of serving its own AI services, most notably Google Translate, says Jeff Dean, the uber-engineer who oversees Google Brain, the company's main AI lab.

In the last ten years, artificial intelligence has changed the world in subtle but sweeping ways, but it's got nothing on the coming decade, if you look at what's being developed today.