'Chatbots are the new apps," said Microsoft's CEO Satya Nadella earlier this year. He was not the first senior tech exec to make this claim. "Threads are the new apps," suggested Facebook's head of messaging products David Marcus in January, referring to the threads of conversation in apps such as Facebook Messenger and WhatsApp. Nadella and Marcus see chatbots – computer programs that you interact with by "chatting", for example in threads in messaging apps – as an important new human/machine interface. Both of their companies have launched tools to help developers create these bots, and between April and September, more than 30,000 were made for Facebook Messenger alone. The shopping and breaking news bots in Messenger's ancestors are chatbots such as AI psychotherapist Eliza from the mid-1960s and Parry, a bot mimicking a human with paranoid schizophrenia, in the early 1970s. Since 1991, the chatbot equivalent of the Olympics has been the annual Loebner prize, which challenges bots ...

A supervised machine learning model aims to learn a function f(x) y from a list of training pairs (x1,y1), (x2,y2), … for which data are recorded (Fig 1B). One typical application in biology is to predict the viability of a cancer cell line when exposed to a chosen drug (Menden et al, 2013; Eduati et al, 2015). The input features (x) would capture somatic sequence variants of the cell line, chemical make?up of the drug and its concentration, which together with the measured viability (output label y) can be used to train a support vector machine, a random forest classifier or a related method (functional relationship f). Given a new cell line (unlabelled data sample x*) in the future, the learnt function predicts its survival (output label y*) by calculating f(x*), even if f resembles more of a black box, and its inner workings of why particular mutation combinations influence cell growth are not easily interpreted. Both regression (where y is a real number) and classification (where y is a categorical class label) can be viewed in this way. As a counterpart, unsupervised machine learning approaches aim to discover patterns from the data samples x themselves, without the need for output labels y. Methods such as clustering, principal component analysis and outlier detection are typical examples of unsupervised models applied to biological data. The inputs x, calculated from the raw data, represent what the model "sees about the world", and their choice is highly problem?specific (Fig 1C). Deriving most informative features is essential for performance, but the process can be labour?intensive

Advances in machine learning have been driven by innovations and ideas from many fields. Inspired by the way that humans learn, Reinforcement Learning (RL) is concerned with algorithms which improve with trial-and-error feedback to optimize future performance. Board games and video games often have well-defined reward functions which allow for straightforward optimization with RL algorithms. Algorithmic advances have allowed for RL to be in real-world problems, such as high degree-of-freedom robotic manipulation and large-scale recommendation tasks, with more complex goals. Twitter Cortex invests in novel state-of-the-art machine learning methods to improve the quality of our products.

When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously. "I can tell you very senior CEOs of major American car companies would shake my hand and turn away because I wasn't worth talking to," said Thrun, now the co-founder and CEO of online higher education startup Udacity, in an interview with Recode earlier this week. A little less than a decade later, dozens of self-driving startups have cropped up while automakers around the world clamor, wallet in hand, to secure their place in the fast-moving world of fully automated transportation. And these companies are hungry for talent and skill sets many don't have. "Uber has just bought a half-a-year-old company [Otto] with 70 employees for almost 700 million," Thrun said.

After decades of stagnation in research and development of artificial intelligence solutions, machine learning has blossomed again – and its seeds are spreading to financial services. Sofia Lotto Persio reports on how AI is improving the field. Almost 20 years later, in March 2016, Google's AlphaGo programme beat South Korean champion Lee Sedol at four matches of Go, a strategy game, in what represents another historic breakthrough for artificial intelligence (AI). In the two-decade span between these achievements, AI has progressed tremendously. Go is a much more complex game than chess, as there are more possible positions on the board than there are atoms in the universe.

NOTE: Part 1 of 2: The following is an excerpt from Thomas International's white paper on Cognitive Fit. It can be found here: http://campaigns.thomasus.com/cognitive/ Employees with strong cognitive ability are a competitive advantage to an organization-- particularly when they are in roles that require quick learning, first-time problem solving, thinking "on your feet" and dealing with ambiguity. People who score well on cognitive ability tests are more likely to develop a greater knowledge of the job more quickly, make effective decisions and successfully reason and strategize to solve problems. In fact, a landmark study from 1998 reviewed 85 years of research and found that higher cognitive ability is linked with higher productivity and performance.

Bayes's Theorem fundamentally is based on the concept of "validity of Beliefs". Reverend Thomas Bayes was a Presbyterian minster and a Mathematician who pondered much about developing the proof of existence of God. He came up with the Theorem in 18th century (which was later refined by Pierre-Simmon Laplace) to fix or establish the validity of'existing' or'previous' Beliefs in the face of best available'new' evidence. Think of it as a equation to correct prior beliefs based on new evidence. One of the popular example used to explain Bayes's Theorem is to detect if a patient has a certain disease or not.

All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at /.keras/keras.json. For instance, if you have set image_dim_ordering tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Weights can be automatically loaded upon instantiation (weights'imagenet' argument in model constructor). Weights are automatically downloaded if necessary, and cached locally in /.keras/models/. Note that using these models requires the latest version of Keras (from the Github repo, not PyPI). Additionally, don't forget to cite Keras if you use these models.

Last week, Google parent Alphabet Inc (NASDAQ:GOOGL) released a report about their new milestone in artificial inteligence [AI] speech recognition that according to the company outperforms existing technology by 50%. Microsoft Corporation (NASDAQ:MSFT) did not want to be left behind as it too announced an amazing breakthrough for this market. According to Xuedong Huang, chief speech scientist for Microsoft, their researchers achieved a word error rate (WER) of 6.3%. It is considered the lowest in the industry. Microsoft recorded the benchmark rate by combining neural network based acoustic and language modeling on the US National Institute of Standards and Technology (NIST) 2000 Switchboard speech recognition task. This is a conversational telephone speech recognition test used as an industry standard.

This Wednesday, the tech billionaire investor announced an investment in Luminance, a newly launched startup that uses artificial technology to read contracts in order help law firms with the arduous process of due diligence for mergers and acquisitions (M&A). It's not a "sexy" piece of technology, Lynch argues -- but one that has huge implications for the way we live our lives, and is indicative of a quiet revolution in artificial intelligence. What this is is probably an example of what's going to be changing a lot of things. If you can get machine technology to be reading contracts, it's going to be changing a lot of the world around us ... you're seeing the beginning of a new age." He has since founded venture capital firm Invoke Capital -- the vehicle through which the investment in Luminance was made. This week, Business Insider sat down with the investor to discuss Luminance, Brexit, his augmented reality plans, and why he likes having an "unfair advantage." Mike Lynch is an investor in Luminance -- but was also instrumental in helping create it. "The bit that makes it possible is the machine learning, and that was being done by some research people at Cambridge, and I actually have a connection because my PhD a long, long time ago was in machine learning," Lynch said. "I was introduced to them, and what they were doing looked great, but I said to them'look, you gotta go and meet some real world people.' "So they started getting real data and they met up with [law firm] Slaughter and May, and basically the machine learnt from Slaughter and May how to do these thing and at that point they made a little company. They got a CEO who is a lady who'd actually been involved in a lot of M&A deals over their career and we funded it, and it's been developing the product, and today it comes out into the bright lights of day."