The greatest bar to wise action and the greatest source of fear is ignorance. A tiny candle gives misleading light and throws huge and ominous shadows. The sun at noon gives great light and throws no shadows. It is time to get this whole problem of men and machines under a blazing noonday beam. Computers will never rob man of his initiative or replace the need for his creative thinking. By freeing man from the more menial or repetitive forms of thinking, computers will actually increase the opportunities for the full use of human reason.

This article is published in collaboration with The Conversation. Once the three-billion-letter-long human genome was sequenced, we rushed into a new "omics" era of biological research. Scientists are now racing to sequence the genomes (all the genes) or proteomes (all the proteins) of various organisms – and in the process are compiling massive amounts of data. For instance, a scientist can use "omics" tools such as DNA sequencing to tease out which human genes are affected in a viral flu infection. But because the human genome has at least 25,000 genes in total, the number of genes altered even under such a simple scenario could potentially be in the thousands.

Support Vector Machine has become an extremely popular algorithm. In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries. All code is available on Github. I'll have another post on the details of using Scikits and Sklearn. SVM is a supervised machine learning algorithm which can be used for classification or regression problems.

On Tuesday, IBM announced that Watson, its cognitive computing system (and former Jeopardy champion), will be spending the next year training for a new job--fighting cybercrime. Watson for Cyber Security is a cloud-based version of IBM's cognitive computing tools that will be the result of a one-year-long research project that is starting in the fall. Students and faculty from eight universities will participate in the research and train Watson to better understand how to detect potential threats. Like many other cognitive systems, Watson learns by digesting large amounts of information. Essentially, the students will train Watson "by annotating and feeding the system security reports and data," according to an IBM press release.

Visit Singularity Hub for the latest from the frontiers of manufacturing and technology as we bring you coverage of Singularity University's Exponential Manufacturing conference. If you've been staying on top of artificial intelligence news lately, you may know that the games of chess and Go were two of the grand challenges for AI. But do you know what the equivalent is for robotics? Just think about how the game requires razor sharp perception and movement, a tall order for a machine. As entertaining as human vs. robot games can be, what they actually demonstrate is much more important.

There's been a perception that the adoption of technology in legal services has been slow over the past few years. However, according to the ALM 2015 Law Tech Survey this is certainly beginning to change as 95% of firm leaders and technologists respondents agreed or mostly agreed with recent decisions by management regarding the firm's technology. Law firms have faced a number of challenges over recent years. Although the UK is recovering well from the 2009 financial crisis this also led to stricter compliance regulations. With the increase in regulatory demands there is a need for better finance and risk mitigation.

I was chatting with an old friend yesterday and he reminded me of a conversation we had nearly 50 years ago. I tried to explain to him what I did for living and he was trying to understand why getting computers to understand was more complicated than key word analysis. I explained about concepts underlying sentences and explained that sentences used words but that people really didn't use words in their minds except to get to the underlying ideas and that computers were having a hard time with that. Fifty years later, key words are still dominating the thoughts of people who try to get computers to deal with language. But, this time, the key word people have deceived the general public by making claims that this is thinking, that AI is here, and that, by the way we should be very afraid, or very excited, I forget which.

Domino's may be forward thinking with its smartphone apps, but Pizza Hut is about to let a damn robot take your order. Pizza Hut Asia and MasterCard are partnering to bring Pepper, SoftBank's somewhat creepy humanoid robot to restaurants by the end of 2016. If all goes to plan, Pepper will be able to take and process entire customer orders. This marks the first commerce application for Pepper, according to MasterCard. It starts with an innocent, friendly hello.

Natural interfaces, with users expressing their needs in natural language, are the new way to engage customers. What dot they all, including chatbots like Facebook M and meeting schedulers like x.ai, have in common? Behind the scenes, algorithms and humans are working in tandem to answer requests. Let's describe it with a simple flight information example, where the user asks "What's the status of FR 8542?". Having people intervene in any of these steps is called Human-in-the-loop.

In the era when everything is getting smarter, AI couldn't have been left behind. Not only do most companies want to have a share in it, they also wish to make it better. For sure technological growth and smart solutions had a lot to do with it. Nowadays researchers have access to much more complex, yet affordable tools. This, mixed with cloud computing and the development of the neural network, makes the studies even more compound.