Without a doubt, 2016 was an amazing year for Machine Learning (ML) and Artificial Intelligence (AI). I have opined on the 5 things to watch in AI for 2017 in another article, however the potential dynamics during 2017 in processor and accelerator semiconductors that enable this market warrant further examination. It is interesting to note that shares of NVIDIA roughly tripled in 2016 due in large part to the company's technology leadership in this space. While NVIDIA GPUs currently enjoy a dominant position for Machine Learning training today, the company's latest quarter growth of 197% YoY, in a market now worth over a half billion dollars, has inevitably attracted a crowd of potential competitors, large and small. And semiconductors remain one of the few pure AI plays for public equity investors seeking a position in this fast growing market.
Uber says it intends to continue a self-driving car test program in San Francisco in defiance of warnings from California's Department of Motor Vehicles that it faces legal consequences for not getting a $150 permit for the project. The state Attorney General's office joined the DMV late Friday in demanding that Uber halt the program immediately. In a letter to Anthony Levandowski, head of the ride-hailing company's automated vehicle team, the state's highest legal office asked Uber to "adhere to California law and immediately remove its'self-driving' vehicles from the state's roadways until Uber complies with all applicable statutes and regulations." Should it fail to do so, "the Attorney General will seek injunctive and other appropriate relief," said Miguel Neri and Fiel Tigno, Supervising Deputy Attorneys General. State rules on autonomous vehicles "don't apply" to Uber's program, Levandowski said in a conference call earlier Friday.
A new wearable artificial intelligence (AI) fitness startup says it can simplify the lives of health-conscious consumers by taking on the tasks of multiple other fitness apps and doing them better. The technology from Boltt, the company in question, uses machine learning algorithms and AI to better process and use data provided by consumers. Automatically collected data such as fitness and movement as well as sleep patterns is added to manually inputted data like calorific intake to give personalised exercise and lifestyle advice, says Aayushi Kishore, co-founder of Boltt Sports Technologies. This should hopefully eliminate many of the current problems with wearables and fitness apps. For example, having to enter data into multiple different apps, not receiving customised advice or feedback and receiving tons of data without any real idea of what to do with it or how to analyse it.