As a technical founder at Siri, Sentient, and Viv Labs, Adam Cheyer has helped design and develop a number of intelligent systems. Drawing on specific examples, Adam reveals techniques he uses to maximize the impact of the AI technologies he employs. Follow O'Reilly on Twitter: http://twitter.com/oreilly

To ease the burden, a group of local entrepreneurs - some of whom are former lawyers - have designed a website that helps lawyers search faster, keep notes and organise their research better. Launched in January, Intelllex, meaning "intelligent law", has already attracted more than 1,000 users - about half of whom are lawyers and the rest law students. Lawyers said it has reduced their research time by 30 to 60 per cent, meaning they can handle more cases. "A junior litigation lawyer spends 35 per cent of his time every day doing research," said Mr Chang.

Legal research can be the bane of every lawyer and law student's existence. From poring over textbooks in law libraries to trawling through cases online and offline to prepare for submissions, it is a process that can take hours. To ease the burden, a group of local entrepreneurs - some of whom are former lawyers - have designed a website that helps lawyers search faster, keep notes and organise their research better. Launched in January, Intelllex, meaning "intelligent law", has already attracted more than 1,000 users - about half of whom are lawyers and the rest law students. The service is currently free, but a subscription fee is likely to be introduced next year.

Your mission We are searching for great machine learning engineers to join the team responsible for: · Extending Criteo's large scale distributed machine learning library (e.g., implementing new distributed and scalable machine learning algorithms, improving their performance) · Building and improving prediction models for ad targeting; proving the business value of the changes and deploying them to production · Gathering and analyzing data, performing statistical modeling You'll have the opportunity to work on highly challenging problems with both engineering and scientific aspects; for example: · Click prediction:ÂHow do you accurately predict in less than a millisecond if the user will click on an ad? Thankfully, you have billions of datapoints to help you. · Offline testing:ÂYou can always compute the classification error on a model predicting the click probability. But will it really correlate with the online performance of this model? · Explore / exploit:ÂIt's easy, UCB and Thomson sampling have low regret. But what happens when new products come and go and when each ad displayed changes the reward of each arm? But what do you do when all data are not equal and when you must distribute the learning overÂthousandsÂof nodes? To qualify for this mission, you need: · MS degree in Computer Science or related quantitative field with 3 years of relevant experience or Ph.D degree in Computer Science or related quantitative field · Good understanding of the mathematical foundations behind machine learning algorithms · Great coding skills.

Bank of America Merrill Lynch released a report this week that said that annual global sales of robots has reached a record 10.7 billion in 2014. The authors valued the overall market for robotic technologies (including related software and sensors) at 32 billion for that same year. By 2020, the authors expect the robot market to be worth 83 billion. The autonomous driverless cars, developed by Google, provide one example of how manual tasks in transport and logistics may soon become automated. The combination of AI, machine learning, deep learning, and natural user interfaces (such as voice recognition) is making it possible to automate many knowledge-worker tasks.

A new robotics startup has launched a sprightly little quadruped robot that is able to scale fences, manipulate door handles, turn back flips, climb stairs and jump up and down while keeping its balance, almost like a robot version of Jiminy Cricket. The Minitaur is a four-legged robot measuring 15.6in by 10.8in that weighs just over 5kg and can carry a payload of over 3kg. The robot features patent-pending gearless direct drive motors that behave like springs and a specialised leg design with sensors that work together to provide precise force feedback, so that the robot can balance and reorient itself from a fall while running and jumping over difficult terrain. Powered by a 72MHz Arduino-compatible robot microcontroller, the robot features high speed and high resolution encoders that enable the robot to sense the ground and process the feedback from the sensors and adapt to situations in real time to keep itself from over-balancing. Minitaur currently retails for 10,000 ( 7,670) and has a maximum running speed of 2m/s and a turning speed of 1 rad/s.

Suman Deb works as the Lead Data Scientist in NY-based startup Studio Betaworks. He is the recipient of the IEEE Communications Society MMTC Best Journal Paper Award in 2015 and the Missouri Honor Medal for Outstanding PhD Research in 2013. He is the editor of IEEE Special Technical Community on Social Networking. Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community.

In this video, I go over some of the state of the art advances in music generation coming out of DeepMind. Then we build our own music generation script in Python using Tensorflow and a type of neural network called a Restricted Boltzmann Machine. The challenge for this video is to generate a happy/upbeat song using the RBM Script. You guys are the reason I do this. If you enjoy my videos, I'd appreciate your support on Patreon:) https://www.patreon.com/user?u

'Looks can be deceiving,' a wise person once said. The phrase holds true for Algorithmic Trading Strategies. The term Algorithmic trading strategies might sound very fancy or too complicated. However, the concept is very simple to understand, once the basics are clear. In this article, I will be telling you about algorithmic trading strategies with some interesting examples. If you look at it from the outside, an algorithm is just a set of instructions or rules. These set of rules are then used on a stock exchange to automate the execution of orders without human intervention. This concept is called Algorithmic Trading.

One of the most important takeways from Davos that quickly became widely spread news, was that the world was about to enter the fourth industrial revolution, resulting from a convergence of a number of big technology changes (autonomous vehicles, sensors, biotechnology, 3D printing, robotics, artificial intelligence). One of the most important technological disruption taking us fast to that extraordinary moment is Deep learning. Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. Making an analogy with the way the brain works, deep-learning software tries to imitate what happens in our brains, more exactly in the layers of neurons in the neocortex, where thinking takes place. Ultimately deep learning software aims to recognize patterns in digital representations of sounds, images, and other data.