As machine learning becomes more accessible through avenues such as Intel Corp.'s BigDL and IBM Corp. opening Watson's core machine learning components up to businesses, some developers and industry insiders are cautioning against getting too dazzled by the potential without considering the human role. However much data those programs can process, in the end, "what you do with the results of algorithms is key," said Jean-Francois Puget, Ph.D. (pictured), distinguished engineer, machine learning and optimization, IBM Analytics, at IBM. Puget spoke with Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media's mobile live streaming studio, at the IBM Machine Learning Launch Event in New York, NY. He offered his perspective on machine learning and its applications. "For most people, machine learning equals machine learning algorithms," Puget said. "When you look at newspapers or blogs, social media, it's all about algorithms. Our view [is] that sure, you need algorithms for machine learning, but you need steps before you run algorithms, and after."

Emerging technologies such as Artificial Intelligence (AI) must be harnessed strategically in national building to provide equal access to opportunities to all, Nadan Nilekani, the former chairman of India's Unique ID Authority, said during a conference in Bengaluru on Monday. Failure to do so will invite a backlash, Nilekani warned, as large numbers of people see themselves being left out with the fruits of technological advancement benefiting only a few. "How do we use all this stuff that you guys have built and flip it around to benefit large numbers of people, and I think that is very important," Nilekani said in a discussion with Satya Nadella, CEO of Microsoft Corp, at a conference on Artificial Intelligence organised by the company. "If we don't do that, I think the backlash, as we've seen in the US, is going to be quite a lot." Nilekani wasn't available for a follow-on discussion, but he was probably referring to many more Americans voting for Donald Trump -- helping Trump become their president -- than any pundits had anticipated, as they feared that his opponent Hillary Clinton represented the elite.

Many machine learning applications require extensive preprocessing on the data for state-of-the-art performance. A large number of preprocessing procedures have to be fused with a learner to create a wrapped learner, which assures that the same preprocessing is used in training and prediction. This creates a preprocessing chain which can be added to any learner with a single command to construct a configurable pipeline. The R programming language already offers a general purpose package for piping function output to new functions, magrittr. This is heavily utilized by the package dplyr for data manipulation, but mostly consists of basic low level functionality, e.g., mutation, aggregation, selection, filtering etc.

Potentially describing how general artificial intelligence will look like. Since scientists started building and training neural networks, Transfer Learning has been the main bottleneck. Transfer Learning is the ability of an AI to learn from different tasks and apply its pre-learned knowledge to a completely new task. It is implicit that with this precedent knowledge, the AI will perform better and train faster than de novo neural networks on the new task. DeepMind is on the path of solving this with PathNet.

Google's ongoing experiments into machine learning are always fascinating, fundamentally reconfiguring the way things are programmed and implying possible sea changes in, well, pretty much any field that involves computers. Still, we are in the early days of this technology, and its impression of humanity is noticeably off. This is particularly true when it attempts art. We have marveled together over a neural network's stomach-churning attempt at writing a Christmas carol--never forget the line "I've always been there for the rest of our lives"--and now Google has released something called the A.I. Duet. As coder and musician Yotam Mann describes above, the technology has absorbed vast quantities of music to create an idea of what relationships of notes sound like together.

In a bid to boost its prospects in the world of artificial intelligence (AI), Apple has acquired Israel-based startup RealFace that develops deep learning-based face authentication technology, media reported on Monday. Reported by Calcalist, the acquisition is to be worth roughly $2 million (roughly Rs. 13.39 crores). A Times of Israel report cites Startup Nation Central to note RealFace had raised $1 million in funding thus far, employed about 10 people, and had sales operations China, Europe, Israel, and the US. Set up in 2014 by Adi Eckhouse Barzilai and Aviv Mader, RealFace has developed a facial recognition software that offers users a smart biometric login, aiming to make passwords redundant when accessing mobile devices or PCs. The firm's first app - Pickeez - selects the best photos from the user's album.

NEW YORK: Spanish lender Banco Santander SA has invested in two artificial-intelligence companies, part of the financial industry's increased focus on technology smart enough to mimic human thinking, sources familiar with the deals told Reuters. The bank's venture arm, Santander InnoVentures, bought stakes in Personetics Technologies, which provides automated customer service, and Gridspace, whose software can learn and interpret language the way a person would, the sources said. The size of the investments could not be determined and the sources asked not to be named because they were not allowed to disclose the information publicly. The deals underscore how lenders have become more interested in using artificial intelligence for a wide variety of tasks, including hiring, spotting fraud, improving call centres and recommending products for customers. Personetics, which has offices in New York, London and Tel Aviv, creates "chatbots" that can respond to customer questions through popular messaging platforms like Facebook Inc's Messenger.

AI is about to go mainstream. It will show up in the connected home, in your car, and everywhere else. While it's not as glamorous as the sentient beings that turn on us in futuristic theme parks, the use of AI in fraud detection holds major promise. Keeping fraud at bay is an ever-evolving battle in which both sides, good and bad, are adapting as quickly as possible to determine how to best use AI to their advantage. There are currently three major ways that AI is used to fight fraud, and they correspond to how AI has developed as a field.

Feedback plc (LON:FDBK) expects to see a substantial increase in revenue from its TexRAD software in the second half of the current financial year. The medical imaging firm has previously reported a significant number of purchase orders for research versions of TexRAD in the first half of its current financial year (i.e. to the end of November), but investors hoping this would show through immediately in the interim results were disappointed. The majority of the installations were done just before the end of the reporting period so the sales made only a modest contribution to revenue. Nevertheless, Feedback trimmed its half-year loss before tax to £130,000 from a loss the year before of £155,000 on revenue that dipped to £204,000 from £225,000. "We are encouraged by the continued strong interest shown in TexRAD and the number of research papers being published, which highlight its numerous potential applications, particularly in areas such as liver, prostate and adrenal cancers," the company said.

Artificial Intelligence (AI) is becoming increasingly integrated into many industries such as healthcare, alongside particular business departments such as sales and customer service. In a society where everything is becoming more technology dependent, it is inevitable that AI will play a role in sales interactions in the future. We expect that sales reps will increasingly use AI in their day to day activities within the next few years, and knowing where and how to take advantage of this new technology will ultimately help sales reps to improve their sales skills and leverage AI to deliver an enhanced service to clients. According to HBR, 85% of all sales rep activities have the potential to be automated (1). Tasks such as gathering information from customers and prospects, taking customers' orders for a particular product or processing the sales transaction itself could all be done by AI technology.