Deep Learning (the favourite buzzword of late 2010s along with blockchain/bitcoin and Data Science/Machine Learning) has enabled us to do some really cool stuff the last few years. Other than the advances in algorithms (which admittedly are based on ideas already known since 1990s aka "Data Mining era"), the main reasons of its success can be attributed to the availability of large free datasets, the introduction of open-source libraries and the use of GPUs. In this blog post I will focus on the last two and I'll share with you some tips that I learned the hard way. TensorFlow is a very popular Deep Learning library developed by Google which allows you to prototype quickly complex networks. It comes with lots of interesting features such as auto-differentiation (which saves you from estimating/coding the gradients of the cost functions) and GPU support (which allows you to get easily a 200x speed improvement using decent hardware).

DeepMind, Alphabet's A.I. research firm, has built an artificial intelligence system capable of defeating a vast majority of the world's StarCraft II players, according to research published Wednesday in the journal Nature. The DeepMind team debuted AlphaStar, its StarCraft II-playing bot, earlier this year in show matches against top esports professionals. But the new research details secret matches held this July with players who opted into being randomly matched against the program. DeepMind deployed three versions of AlphaStar, which each learned the game in a slightly different way. The first two versions of AlphaStar were good enough to reach the highest tier of play, Grandmaster.

" The two key ideas of deep learning for computer vision -- convolutional neural networks and backpropagation were already well understood in 1989. The Long Short Term Memory (LSTM) algorithm, which is fundamental to deep learning for timeseries, was developed in 1997 and has barely changed since. So why did deep learning only take off … Continue reading Why deep learning?

Taking inspiration from the way pro StarCraft II players train with one another, the researchers instead programmed one of the algorithms to expose the flaws of the other rather than maximize its own chance of winning. "That's kind of [like] asking a friend to play against you," said Oriol Vinyals, the lead researcher on the project, on the call. "These friends should show you what your weaknesses are, so then eventually you can become stronger." The method produced much more generalizable algorithms that could adapt to a broader range of game scenarios.

Most people find it a pain to receive parcels in wide time slots like, say, between 8 a.m. and 5 p.m., so when delivery startup Paack offered a service in which everyone could narrow that window down to one hour, with no extra charge, it had a challenge on its hands. The startup's routing engine worked but needed to be more efficient. Enter Prowler.io, a Cambridge, U.K.-based machine-learning startup that bills itself as a decision-making platform for any company with complex problems to solve. Paack's contact at venture firm Balderton in London introduced it to Prowler in February 2018, and within months its delivery vans and trucks were being coordinated by an intelligent, digital simulation. With the beta test over, Paack's CEO, Fernando Benito, sees a potential benefit to his bottom line. Some of his startup's deliveries are now 15% more efficient, he tells Forbes.

On October 25, 2019, Google changed everything again with its announcement that its deep learning powered natural language processing model, BERT, would now power search queries. With this announcement, machine learning expertise for SEO is no longer optional. Let's dig into what this all means. BERT is a massive language model that helps machine learning algorithms understand and predict sequences of words, phrases, sentences, and concepts. BERT stands for Bidirectional Encoder Representations from Transformers, open-sourced by Google in 2018.

However, integrating predictive analytics with business processes is not an entirely straightforward process. Prior to any modelling activities, data science teams, business teams and IT teams have to understand fully the business needs and related technology needed to deploy the models into their core systems. Otherwise, problems will arise when insurers move to operationalize their analytics and the expected business value is often never realised. Moreover, even artificial intelligence deep-learning techniques – that is, artificially intelligent systems capable of learning unsupervised by looking at unstructured data – may struggle to understand patterns they have never seen before. Depending on the insurer's needs, and the nature of the data they are using (structured or unstructured) variable amounts of data are required to create models.

Musk is the face behind numerous tech companies, including SpaceX, Tesla, Neuralink, The Boring Company and OpenAI. During an event at Stanford University in 2015, the entrepreneur was asked by DFJ General Partner Steve Jurvetson what he thought the year 2035 would look like. The 48-year-old entrepreneur did not disappoint.

With data taken from "the front page of the Internet", this guide will introduce the top 10 Reddit datasets for machine learning. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language... Researchers from Russian corporation Neurobotics and the Moscow Institute of Physics and Technology have found a way to visualize a person's brain activity as actual images mimicking what they observe in real time. This will enable new post-stroke... Amazon is hiring more developers for Alexa than Google for everything. I explain the theory of "margins" that serves as a theoretical explanation for why AdaBoost has been observed not to overfit in practice.

DeepMind's AlphaStar AI bot has reached Grandmaster level at StarCraft II, a popular battle strategy computer game, after ranking within the top 0.15 per cent of players in an online league. StarCraft II is a complex game and has a massive following with its own annual professional tournament - StarCraft II World Championship Series - that involves the best international teams competing over a prize pot over $2m. AlphaStar, however, isn't quite good enough to compete in that competition. Instead it set its eyes on a much smaller contest on Battle.net, the game's official online league hosted by China-friendly gaming biz Blizzard Entertainment. Researchers at Google-stablemate DeepMind entered its bot AlphaStar into a series of blind games, where its opponents had no idea it was playing against a computer.