Ed. note: This is the latest installment in a series of posts from Lateral Link's team of expert contributors. Michael Allen is Managing Principal at Lateral Link, focusing exclusively on partner placements with Am Law 200 clients and placements for in-house attorneys. There's a new attorney named ROSS in BakerHostetler's bankruptcy practice and it doesn't eat, sleep, or complain about bonuses. Back in May, the Texas firm announced it would be the first to integrate artificial intelligence into its practice. Since then, Latham has entered into the fray, along with the Milwaukee-based Von Briesen & Roper.

There is no doubt that neural networks, and machine learning in general, has been one of the hottest topics in tech the past few years or so. It's easy to see why with all of the really interesting use-cases they solve, like voice recognition, image recognition, or even music composition. So, for this article I decided to compile a list of some of the best Python machine learning libraries and posted them below. The last point here is arguably the most important. The algorithms that power machine learning are pretty complex and include a lot of math, so writing them yourself (and getting it right) would be the most difficult task.

If you are a Python programmer or you are looking for a robust library you can use to bring machine learning into a production system then a library that you will want to seriously consider is scikit-learn. In this post you will get an overview of the scikit-learn library and useful references of where you can learn more. Scikit-learn was initially developed by David Cournapeau as a Google summer of code project in 2007. Later Matthieu Brucher joined the project and started to use it as apart of his thesis work. In 2010 INRIA got involved and the first public release (v0.1 beta) was published in late January 2010.

If the industrial world wants to lure millennial grads to the manufacturing floor, maybe they should tell the grads the factory has become a video game. In his session at the ATX Automation Technology Conference in Minneapolis on Sept. 21, Tom Geiger, head of electric automation, Americas at Festo, will explore the boundaries of automation. He--ll describe a not-too-distant future of robotics and machine learning spun together like a virtual reality game. Geiger--s session, Working Smarter, Not Harder: Human Collaboration with Robotics and Automation, will look at science-fiction-like issues such as moving from machine learning to cognitive computing that will enable robotics and automation to make creative decisions in case of planned or unplanned changes. Geiger envisions a plant where machines can order their own supplies when needed, eliminate repetitive tasks, enlarge manufacturing capacity, and provide faster output -- all by themselves. Geiger explains the plant he envisions would be operated much like a video game, where the operators move machine data around on a computer screen and the equipment on the floor responds.

Sorry if this is too trivial, but let me start at the "very beginning:" Linear regression. The goal of (ordinary least-squares) linear regression is to find the optimal weights that -- when linearly combined with the inputs -- result in a model that minimizes the vertical offsets between the target and explanatory variables, but let's not get distracted by model fitting, which is a different topic;). So, in linear regression, we compute a linear combination of weights and inputs (let's call this function the "net input function"). Next, let's consider logistic regression. Here, we put the net input z through a non-linear "activation function" -- the logistic sigmoid function where.

There's no single book that can help you fully master machine learning. It's a complicated subject that spans many topics, purposes, and of course benefits in real-world applications. But this post should help novices and experts alike find the right book to continue their education. With so many resources available it can be tough knowing where to start. I'll consider 10 books and look at each one's teaching style, subject matter, and recency of publication.

Recently popularized by IBM's highly intelligent Watson supercomputer, which competed on the hit game show Jeopardy, cognitive computing refers to machines that are capable of learning concepts and patterns through advanced language processing algorithms. A system that involves incredibly advanced artificial intelligence, cognitive computing is one facet of computer science that isn't for the faint of heart. Although much of the hype around cognitive computing is centered on big business and big data processing, there are a number of consumer applications. Whereas business leaders might use the technology to increase their bottom line, streamline daily operations and achieve greater profitability, consumers can take advantage of cognitive computing to ease some of the burdens of everyday life. In fact, many consumers are using some form of cognitive computing without realizing it.

IBM Research is a Lab and Center member of CRA. This article is the first in a series of our industry member profiles. It's not surprising that the public's imagination has been ignited by artificial intelligence since the term was first coined in 1955. In the ensuing 60 years, we have been alternately captivated by its promise, wary of its potential for abuse, and frustrated by its sometimes slow development. But like so many advanced technologies that were conceived before their time, artificial intelligence has come to be widely misunderstood--co-opted by Hollywood, mischaracterized by the media, and portrayed as everything from savior to scourge of humanity.

Artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. An umbrella term that refers to information systems inspired by biological systems, AI encompasses multiple technologies including machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. According to a new report from Tractica, these technologies have use cases and applications in almost every industry and promise to significantly change existing business models while simultaneously creating new ones. The market intelligence firm forecasts that annual worldwide AI revenue will grow from 643.7 million in 2016 to 36.8 billion by 2025. In sizing and forecasting the total global AI market, Tractica has identified 191 real-world use cases for AI, organized into 27 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies.

The system, known as WaveNet, is able to generate human speech by forming individual sound waves that are used in a human voice. Additionally, because it is designed to mimic human brain function, WaveNet is capable of learning from extremely detailed -- at least 16,000 samples per second -- audio samples. The program statistically chooses which samples to use and pieces them together, producing raw audio. While most of the existing TTS systems also use the same "piece by piece" idea, they largely utilize concatenative TTS. Despite drawing from a large database, these systems are restricted to combinations of short recorded speech fragments from a single speaker, which makes modifying the voice or its inflection difficult.