Shares of graphics chip maker Nvidia (NVDA) are down 6 cents at 35.69, following yesterday's annual meeting with analysts. A webcast replay of presentations CEO Jen-Hsun Huang and other executives, and the Q&A, can be viewed from the company's investor relations page. Huang made the pitch that with new frontiers of machine learning and artificial intelligence, Nvidia "are no longer a co-processor," meaning a handmaid to the PC microprocessor. "There is no workload we run," said Huang, such as a video game. Instead, he said, with the company's programming technology, "CUDA," "we run an application that a developer writes on top of it."

NVIDIA has released a new state-of-the-art chip that pushes the limits of machine learning. The Tesla P100 GPU, which CEO Jen-Hsun Huang revealed yesterday at NVIDIA's annual GPU Technology Conference, can perform deep learning neural network tasks 12 times faster than NVIDIA's previous top-end system. The P100 was a huge commitment for NVIDIA, costing over 2 billion in research and development, and it sports a whopping 150 billion transistors on a single chip, making the P100 the world's largest chip, NVIDIA claims. In addition to machine learning, the P100 will work for all sorts of high performance computing tasks -- NVIDIA just wants you to know it's really good at machine learning . To top off the P100's introduction, NVIDIA has packed eight of them into a crazy-powerful 129,000 supercomputer called the DGX-1, which was also announced yesterday.

We are recruiting the Machine Learning Expert to strengthen our tech team. You will be responsible for researching and developing machine learning algorithms and AI expert systems to find the best match between talents and job offers. You will be working with big data scrapped from different public sources around the Internet. You will choose the tools and take the responsibility for it. Frameworks and tools we use today in backend development: Mysql, Node, Sequelize, Git, Pivotal Tracker.

Seoul: South Korea's top automaker Hyundai Motor and its affiliate Kia Motors on Tuesday announced a plan to develop artificial intelligence (AI)-based, internet-connected car to create a new future lifestyle with a "driving, high-performing computer". The driving computer means a car will become a high-performing computer itself as the car, to be developed by Hyundai and Kia, will self-drive based on AI and connect to electronic devices while driving based on internet connectivity, Xinhua cited a joint statement as saying. The main concept of the project is a "hyper-connected and intelligent car", which means an interaction between cars home as well as home and office in addition to AI-based self-driving. To achieve the goal, the automakers will focus on four major themes, intelligent remote-controlling support service, perfect self-driving, smart traffic and mobility hub. The remote-controlling support service aims to check and examine cars on a real-time basis to detect potential emergency situations in advance.

The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as "knowledge discovery in databases," the term "data mining" wasn't coined until the 1990s. But its foundation comprises three intertwined scientific disciplines: statistics (the numeric study of data relationships), artificial intelligence (human-like intelligence displayed by software and/or machines) and machine learning (algorithms that can learn from data to make predictions). What was old is new again, as data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power. Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights.

Baltimore, MD - Alex Zhavoronkov, PhD, CEO of Insilico Medicine will present a range of deep learned biomarkers of ageing and deep learned predictors of biological age at the RE-WORK Deep Learning in Healthcare Summit in London, 7-8th of April. The first such predictor is already available online at http://www.Aging.AI trained on hundreds of thousands of human biochemistry and cell count samples linked to chronological age, gender and health status. Transcriptomic and signalomic ageing markers and predictors of chronological and biological age and cross-species comparison will be discussed. "RE-WORK summits are clearly outperforming most industry conferences in agility, openness, diversity and focus on applications of deep learning in multiple areas and we are happy to be invited to present at their Deep Learning in Healthcare Summit in London. Artificial intelligence will transform biomarker development and drug discovery much sooner than most pharmaceutical companies and regulators expect and we are happy to be at the forefront of this emerging trend", said Alex Zhavoronkov, PhD, CEO or Insilico Medicine, Inc.

We've covered robot writers before, but to date it has never been much of a concern for actual writers. Robots just aren't that good at doing what we do; although Microsoft's'Tay' did prove to be pretty great at going from zero to off-the-rails after dealing with some nasty Twitter comments -- something any writer can relate to. Until now, "robot writers" -- artificial intelligence programs taught to write -- were mainly only good at penning quick stories based on data-heavy reports. Box scores, stock reports, and the like were basically all the programs were capable of doing well. This year's edition of TNW Conference in Amsterdam includes some of the biggest names in tech.

One of the world's biggest banks has revealed it is leading the fight against criminals using state of the art AI technology. The Bank of Russia has teamed up with Yandex Data Factory to use the latter's machine learning services to identify unlicensed money lenders, and the websites hosting them. So far, Yandex Data Factory's custom search algorithm has helped to reveal 2,500 suspicious organisations, and played a significant role in spotting any inaccuracies, meaning that potentially fraudulent organisations will not be missed in the future. The system works by analysing key words across an existing Yandex database, which identified seven million web pages related to finance topics. The algorithm is then able to assign a web page to its correct category, before identifying if an organisation is licenced, which Yandex says occurred correctly in 98 percent of cases.

Messaging app Kik has launched an online store for chatbots, to allow brands to talk to users via bots. Chatbots - computer programs designed to simulate conversation with humans - are increasingly being seen as a key way to engage customers.

Mitch De Felice recently wrote in CIO.com, "As a technology decision maker, all the vocabulary of artificial intelligence might be a bit overwhelming. In Figure 1 [to the left], starting from the bottom going up illustrates knowledge acquisition capabilities from a data usage perspective. By no means does this represent all the approaches to achieving an AI solution, but rather it illustrates how big data fits into the AI picture. Machine learning is represented by the right side of the above diagram, labeled, 'Statistical Reasoning.' There are two types of machine learning, unsupervised and supervised. When big data vendors speak of machine learning, they are usually speaking of supervised machine learning that has existed since the 1950s."