Impact of automation on lawyers and doctors. Apple sees its mobile devices as platform for AI. Machine learning used to predict earthquakes in a lab setting. http://cognitionx.com/news-briefing/.
Over the past few days, there has been a fair amount of news about AI in China, from large funding rounds to sticky ethical questions surrounding AI’s use in monitoring the population. What do you think: will China become the global leader in AI?
In the next five to 10 years, there could be a 10 to 20 percent reduction in jobs for physicians. That’s according to Vivek Wadhwa, a distinguished fellow at Carnegie Mellon University’s College of Engineering in Silicon Valley and author of a new book, “The Driver in the Driverless Car,” which details our increasingly automated future and what it could mean for jobs, equality, and dependence.
“It’s a data game,” he said, adding that “there will be massive amounts of algorithms” able to diagnose disease and recognise patient irregularities in the coming years. Wadhwa visualises a future in which patients store their personal data digitally through a cloud storage service and take it with them everywhere.
Apple sees its mobile devices as a major platform for artificial intelligence in the future, Chief Operating Officer Jeff Williams said on Monday. The phone promises new facial recognition features such as Face ID that uses a mathematical model of a person’s face to allow the user to sign on to their phones or pay for goods with a steady glance at their phones.
“We think that the frameworks that we’ve got, the ‘neural engines’ we’ve put in the phone, in the watch … we do view that as a huge piece of the future, we believe these frameworks will allow developers to create apps that will do more and more in this space, so we think the phone is a major platform,” Williams said. He was speaking at top chip manufacturer Taiwan Semiconductor Manufacturing Company’s 30th-anniversary celebration in Taipei, which was attended by global tech executives.
A group of researchers from the UK and the US have used machine learning techniques to successfully predict earthquakes. Although their work was performed in a laboratory setting, the experiment closely mimics real-life conditions, and the results could be used to predict the timing of a real earthquake.
The team, from the University of Cambridge, Los Alamos National Laboratory and Boston University, identified a hidden signal leading up to earthquakes and used this ‘fingerprint’ to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters.
A new digital tool tracking how autonomous vehicles are being deployed and tested across the world went online yesterday, and it’s an interesting snapshot of where we are right now with this new technology. The Global Atlas of Autonomous Vehicles in Cities, a joint effort between Bloomberg Philanthropies and the Aspen Institute, shows which city governments are testing AVs, and more importantly, it shows how few cities are preparing for the onslaught of self-driving cars that is expected in the next decade.
According to the map, 53 cities are testing or thinking about testing AVs. Of that number, 35 cities including San Francisco, Austin, Nashville, Washington, Paris, Helsinki, and London are already piloting projects. Another 18 cities, such as Los Angeles, Tel Aviv, Buenos Aires, and Sao Paulo, are undertaking surveys or assessing the implications of AVs. The groups plan to update the map in real-time as more pilot projects involving self-driving cars come online.
For several reasons, quantum computers could prove quite beneficial with more widespread application. They could help with a multitude of complex issues, from things like creating solutions for climate change to organizing massive sets of data about health care.
“As companies such as Microsoft, Google, and IBM continue to develop technologies such as this, dreams of quantum computing are becoming a reality,” writes Daryl Harrington for InfoWorld. “This technological innovation is not about who is the first to prove the value of quantum computing. This is about solving real-world problems for our future generations in hopes of a better world.”
The average company faces many challenges in getting started with machine learning, including a shortage of data scientists. But just as important is a shortage of executives and nontechnical employees able to spot AI opportunities. And spotting those opportunities doesn’t require a PhD in statistics or even the ability to write code. (It will, spoiler alert, require a brief trip back to high school algebra.)
Kathryn Hume describes how having an intuition for how machine learning algorithms work – even in the most general sense – is becoming an important business skill. Machine learning scientists can’t work in a vacuum; business stakeholders should help them identify problems worth solving and allocate subject matter experts to distill their knowledge into labels for datasets, provide feedback on output, and set the objectives for algorithmic success.
Word embeddings have established themselves as an integral part of Natural Language Processing (NLP) models. In other aspects, we might as well be in 2013 as we have not found ways to pre-train word embeddings that have managed to supersede the original word2vec.
This post focuses on the deficiencies of word embeddings and how recent approaches have tried to resolve them. If not otherwise stated, this post discusses pre-trained word embeddings, i.e. word representations that have been learned on a large corpus using word2vec and its variants. Pre-trained word embeddings are most effective if not millions of training examples are available (and thus transferring knowledge from a large unlabelled corpus is useful), which is true for most tasks in NLP. For an introduction to word embeddings, refer to this blog post.