Cognitive automation is an exciting future trend and we are at the forefront in helping our clients understand the implications for their organisation. KAI (short for KPMG Artificial Intelligence) is a Furhat Robotics artificial intelligence robot head and one of the newest components supporting the digital labour proposition and our wider cognitive automation agenda as a whole. KAI is a ground breaking user interface that replicates a human with social intelligence, allowing interaction between humans and computers. KAI's'brain' is powered by a state-of-the-art conversational intelligence platform which is used to build highly sophisticated and socially aware spoken interactions with the robot head. We have started to showcase KAI at various internal events and external presentations, including the recent Digital Labour Symposium and the Solutions Showcase. KAI has also given introductory presentations at client events. So far, we have used KAI in limited, controlled environments where his interactions are pre-programmed. The next step is to develop a back-end engine that will enable KAI to respond to and answer questions that a user poses. The data science team is currently working on this and we hope to have a live demo in the near future.

The past 30 years have seen incredible growth and innovation in the tech industry. We've gone from pocket calculators and PCs to pocket computers more powerful than the mammoth mainframe computers of the 1980s. The Atari 800XL computer I used in high school to develop games was powered by a microprocessor with 3,500 transistors – the one running my iPhone today has 2 billion transistors. The cost of a gigabyte of storage was in the range of 100,000 and the size of a refrigerator. Today, it's basically free and size is measured in millimetres.

Your next financial adviser might be a centaur -- not half-human, half-horse, but half-human, half-machine. You might have heard of centaurs in chess, where a person and a computer form a team that together can beat the best human or computer playing alone. The idea, popularized after IBM's computer Deep Blue learned chess well enough to defeat world champion Garry Kasparov in 2003, is infiltrating financial planning. In artificial intelligence or machine learning, computers effectively program themselves to analyze data and solve problems. That's how IBM's Watson beat champions Ken Jennings and Brad Rutter on the television quiz show Jeopardy in 2011.

We all become accustomed to the tone and pattern of human speech at an early age, and any deviations from what we have come to accept as "normal" are immediately recognizable. That's why it has been so difficult to develop text-to-speech (TTS) that sounds authentically human. Google's DeepMind AI research arm has turned its machine learning model on the problem, and the resulting "WaveNet" platform has produced some amazing (and slightly creepy) results. 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 learni... Artist Ai Weiwei poses next to images of Andy Warhol at the Museum of Modern Art in 1987.

Symmetry Master evaluated they symmetry of each person's face and AntiAgeist estimated the difference between the chronological and perceived age. Once these parameters were determined, the fifth robot, called MADIS, compared each selfie to models and actors within their age and ethnic groups were are stored in a database. 'We are very pleased with the Ai's performance in achieving 100 percent accuracy in predicting the I'm a Singer competition's results,' Dr. Min Wanli, Alibaba Cloud's chief scientist for artificial intelligence, said in a statement following the show, according to the Wall Street Journal. '[The result] is very random and almost impossible to predict using human intelligence,' said Min Wanli, chief scientist for artificial intelligence at Alibaba Cloud.

Artificial intelligence and shopping -- does that mean robots that'll stock the shelves? We'll have robots that will use A.I. to check inventory, help customers find the items they're shopping for, ferry supplies from one part of the warehouse to another, aid with shipping, you name it. But the real revolution for A.I. and shopping will be invisible because the technology will create better experiences for consumers while helping employees and shopkeepers run operations more effectively. Let's take any big department store that sells thousands of home, fashion, and beauty products -- more specifically, let's say that there are exactly 100,000 products. Because customers will buy up to 150 percent more and be happier with their purchases if they're shown the items in context, merchants will create outfits, design window displays, and produce splashy catalogs and digital lookbooks to help customers imagine how to wear the latest fashion trend, how to arrange their living rooms to show off their new velvet sectional, or how to install an outdoor shower.

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.

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.

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 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.