CBInsights have just released a 92-page report on the state of AI which is definitely worth a read. The report covers AI M&As (200+ since 2012), analyses AI-related patents, reports on media trends, profiles companies working on general AI, and much more.
He writes that in 2017, we’re now well into this deployment phase. The term “Big Data” continues to gradually fade away, but the Big Data space itself is booming. We’re seeing everywhere anecdotal evidence pointing to more mature products, more substantial adoption in Fortune 1000 companies, and rapid revenue growth for many startups. He concludes by saying that “With the killer combination of Big Data and AI, we’re heading towards the ‘harvesting’ part of the cycle. Beyond all the hype, the possibilities are enormous.”
Vijay Pande, professor of chemistry at Stanford University, and his students thought that a fairly new kind of deep learning, called one-shot learning, that requires only a small number of data points might be a solution to the low-data problem of drug development.
“We’re trying to use machine learning, especially deep learning, for the early stage of drug design,” said Pande. “The issue is, once you have thousands of examples in drug design, you probably already have a successful drug.”
Over the next three weeks, about 100 people will travel in a prototype shuttle on a route in Greenwich, London. The vehicle, which travels up to 10mph (16.1kmph), will be controlled by a computer. However, there will be a trained person on board who can stop the shuttle if required during the tests.
Oxbotica, the firm that developed the technology behind the shuttle, said 5,000 people had applied to take part. “Very few people have experienced an autonomous vehicle, so this is about letting people see one in
person,” chief executive Graeme Smith told the BBC.
Alibaba has launched a smart customer service chat bot powered by AI that retailers can customise to suit their individual virtual-storefront operations.
Named Dian Xiaomi (store assistant), the text-only chat bot was inspired by Ali Xiaomi, an AI-powered chat bot rolled out by Alibaba in 2015 to handle customer enquiries and complaints coming into the e-commerce company. Encouraged by the success of Ali Xiaomi in understanding and answering questions from human users, Alibaba said it wanted to make the
technology available to merchants who sell through its online marketplaces so they can upgrade their customer service.
Predicting earthquakes, producing music, lip-reading and describing photos are just a few things that deep learning can do. Yaron Hadad‘s list links you to articles and videos so that you can explore the topics that interest you.
Google has been using compute-intensive machine learning in their products for the past 15 years. They use it so much that they have even designed an entirely new class of custom machine learning accelerator, the Tensor Processing Unit.
Just how fast is the TPU, actually? Yesterday, they released a study that shares new details on these custom chips, which have been running machine learning applications in our data centers since 2015. Details include: 1) TPU is 15x to 30x faster than contemporary GPUs and CPUs and 2) The neural networks powering these applications require a surprisingly small amount of code: just 100 to 1500 lines.
This week, Stanford put their course on NLP with deep learning by Christopher Manning and Richard Socher up on Youtube. The course features 18 lectures and tackles a wide range of topics such as word vector representations and neural machine translation.
Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeric vectors is then introduced, and popular approaches to designing word vectors are discussed.
The global machine learning as a service (MLaaS) market is poised to grow from $1.07 billion in 2016 to $19.86 billion in 2025, at a CAGR of more than 38%, according to a new report from Transparency Market Research.
Demand for MLaaS has been highest in the healthcare and life sciences industry, due primarily to the need to
integrate structured and unstructured data in these areas, especially data generated by electronic health records. Other industries that will benefit from this technology moving forward include manufacturing, retail, telecom, finance, energy and utilities, education, and the government, as MLaaS can improve the decision-making capabilities of devices used in those areas, the report stated.