With, left, Estonian e-residency project head Kaspar Korjus, Veriff CEO Kaarel Kotkas: "There are some differences that a human eye could miss, but the algorithm won't." In the spring last year, Kaarel Kotkas was finishing his high-school studies at the Tallinn Secondary School of Science. Around that time he was approached by billion-dollar startup TransferWise, which has its development hub in Estonia. "We've heard great things about you. Could we meet up and discuss a few opportunities for cooperation?"

Obama administration officials are previewing long-awaited guidance that attempts to bring self-driving cars to the nation's roadways safely -- without creating so many roadblocks that the technology can't make it to market quickly. Traditional automakers and tech companies have been testing self-driving prototypes on public roads for several years, with a human in the driver's seat just in case. The results suggest that what once seemed like a technology perpetually over the horizon appears to be fast approaching, especially with car companies announcing a string of investments and acquisitions in recent months. Federal officials have been struggling with how to capitalize on the technology's promised safety benefits -- the cars can react faster than people, but don't drink or get distracted -- while making sure they are ready for widespread use. The new guidance represents their current thinking, which they hope will bring some order to what has been a chaotic rollout so far.

We @ProjectJunoAI are big fans of landscapes. That's why we've created a machine intelligence landscape focused entirely on Europe [1]. Europe deserves a landscape of its own to highlight its talent and expertise. Until recently, its contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO.

If you are able to read and sufficiently comprehend any text in just a few seconds, you have reached the zenith of a prime human endeavor. For other readers who clock near the average of 230 words per minute and often find yourselves in situations where time is not a luxury, you may find Textdiver very useful. Textdiver is a free online tool developed by Scope Labs that uses Natural Language Processing (NLP), Text Statistics and Data Mining to uncover hidden trends and insights within any text. While the true meaning of a text is very difficult to ascertain with even the most advanced NLP techniques, Textdiver offers readers a content summary based on the text structure and word distribution, from which keywords and core concepts can be inferred. What sets Textdiver apart from many tools is that it goes a step further in offering more comprehensive metrics beyond Word and Character Count. These include: Word Frequencies, Average Word Length, Average Number of Syllables Per Word, Sentence Count, Sentence Length Distribution, Average Sentence Length, Readability (years of formal education needed to understand a text as estimated by the SMOG index), Lexical Density (percentage of content words), Lexical Diversity (percentage of unique lexical words), Estimated Reading Time, Estimated Speaking Time, Average Typing Speed and Webpage Scraping functionalities.

Google's DeepMind announced the WaveNet project, a fully convolutional, probabilistic and autoregressive deep neural network. It synthesizes new speech and music from audio and sounds more natural than the best existing Text-To-Speech (TTS) systems, according to DeepMind. Speech synthesis is largely based on concatenative TTS, where a database of short speech fragments are recorded from a single speaker and recombined to form speech. This approach isn't flexible and can't be adjusted to new voice inputs easily, often resulting in the need to completely rebuild a dataset when there's a desire to drastically alter existing voice properties. DeepMind notes that while previous models typically hinge around a large audio dataset from a single input source, or single person, WaveNet retains its models as sets of parameters that can be modified based on new input to an existing model.

The artificial intelligence offshoot of Google has paired up with University College London Hospitals NHS Foundation Trust for a research project. DeepMind Health announced that it will be receiving anonymised data from the trust for a research partnership into head and neck cancer. The five-year collaboration will use around 700 anonymised CT and MRI scans of former patients, dating back to 2008, and additional data relating to approximate age, anatomy location, cancer type and radiotherapy received. Currently before radiotherapy can be administered, clinicians take up to four hours to identify and differentiate between cancerous and healthy tissues on CT and MRI scans of head and neck cancer patients. This process is called segmentation.

D-RAFT Corporate Demo Days are designed to connect best ready-to-scale, late stage startups in one area with corporations that want to buy, partner or invest in disruptive solutions. We invite interested companies from the portfolios of leading incubators, accelerators and VCs in Europe. Ten selected startups will receive the opportunity to present their solutions (including: AI & digital disruption, big data & machine learning, human to technology, exponential technologies) in a 8 min.

Neural networks are very powerful tools to classify data but they are very hard to debug. Indeed, they do a lot of computation with low level operations so they are like black boxes: we provide inputs and get outputs without any understanding on how the neural network is finding the results. Few years ago some scientists found ways to delve into the networks used for image categorization. Instead of doing backpropagation on weights like during the learning phase of a neural network, they did backpropagation on the images themselves: in the example below (edited from CS231n), considering x are inputs and w are weights, each learning step, the gradient (red) is applied to the x instead of the w. In this article, we will use the method and code from Google, Simonyan, Yosinski and Chollet to try to visualize the classes and convolutional layers learnt by popular neural networks. The code provided in this article uses the Keras library.

New drugs typically take 12-14 years to make it to market, with a 2014 report finding that the average cost of getting a new drug to market had ballooned to a whopping 2.6 billion. It's a topic I've covered before, with a study published earlier this year highlighting how automation could be used to reduce the cost of drug discovery by approximately 70%. It's an approach that a number of companies are taking to market. For instance, London based start-up Benevolent.AI utilizes complex AI to look for patterns in the scientific literature. They have already managed to identify two potential drug targets for Alzheimer's that has already attracted the attention of pharmaceutical companies.

Microsoft says it is going to "solve" cancer in the next 10 years. The company is working at treating the disease like a computer virus, that invades and corrupts the body's cells. Once it is able to do so, it will be able to monitor for them and even potentially reprogram them to be healthy again, experts working for Microsoft have said. The company has built a "biological computation" unit that says its ultimate aim is to make cells into living computers. As such, they could be programmed and reprogrammed to treat any diseases, such as cancer.