South Korean Conglomerate LG Electronics announced that it will heavily invest in robot technology and artificial technology according to a report by Reuters Sunday. LG, Samsung's rival, is seeking to take advantage of recent advances in artificial intelligence which have the potential of creating sophisticated machines that can perform mundane everyday human tasks. LG, in a statement, said that its appliances division is preparing to enter the robotics industry, like Apple, Google, and Samsung, to create products which can integrate with their appliances products such as refrigerators, washers and air conditioning units. "We will prepare for the future by aggressively investing in smart home, robots and key components and strengthen the home appliances business's capabilities," said Jo Seung-jin, head of LG's appliances business, in the statement. Advances in artificial intelligence and wireless communications are making it possible for machines that are wifi-enabled to exchange information with each other and perform independent tasks.

Toshiba Corp. and Canon Inc. are starting separate drone-based services for corporate clients. Toshiba said Monday it will partner with auto parts manufacturer Alpine Electronics Inc. to commercialize a system by March 2018 to inspect remote infrastructure such as power cables and steel towers. Their service will combine Alpine Electronics' satellite navigation knowledge and Toshiba's digital image processing and artificial intelligence technologies. Camera and printer maker Canon Inc. meanwhile announced on Monday an investment in Prodrone Co., an Aichi Prefecture-based firm that manufactures drones for industrial use. Canon and Prodrone plan to sell drones loaded with Canon's cameras for farming, infrastructure inspection and surveillance purposes.

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context.

There are few subjects in computing as fascinating, or intimidating, as machine learning. Let's face it -- mastering machine learning isn't something you can do in a weekend, and at the very least it requires a good grasp of the underlying mathematical principles. That said, if you've got the math chops, you'll want to augment your use of machine-learning frameworks (of which there are plenty to pick from now) with a good understanding of the theory behind them. Here are five high-quality, free-to-read texts that provide introductions to and explanations of machine learning's ins and outs. Some have code examples, but most focus on formulas and theory, meaning they can in principle be applied to any number of languages, frameworks, or problems.

Stare across the gallery of the Tate Britain at a Turner and you bring with you a quiet murmur of experiences – a family holiday, an orgasm, that morning's news. What you see works in co-ordination with echoes. But what does a computer see? What does it think when it looks at a Turner? Recognition, the winner of the Tate's IK Prize 2016 for digital innovation, asks – and answers – some of these questions. Made up of multiple artificial-intelligence technologies, Recognition makes use of object recognition, facial recognition and composition analysis to pull up-to-the-minute photojournalism from Reuters, then trawls through Tate Britain's vast collection to plonk a comparable picture beside it.

A study titled, 'Hundred Year Study on Artificial Intelligence', (A100), being carried out by Stanford University aims to know the impact of artificial intelligence on our daily life. The study carried out by 17 experts started with a report'Artificial Intelligence and Life in 2030' that mentioned as to how advancements like drones and autonomous vehicles would merge with society. The panel that includes members from Google, Facebook, Microsoft and IBM has planned to make changes in the report with updates after every five years. Majorly, it is being considered that self-driving would be the one to usher the technology into mainstream. Report's co-author Peter Stone from the University of Texas at Austin and colleagues said that AI would replace human workers at one go.

Americans have a pressing problem: Nobody is saving money. Consider this: One out of every three people has a grand total of zero dollars saved, and 63 percent of Americans do not have enough money saved to cover an unexpected 500 expense. That s bad enough, but these folks are also going to live a lot longer than prior generations all these people drinking kombucha and eating kale chips aren t saving anywhere near enough to pay for the long, long lives they have ahead of them. So, what should we do? Should we put more pressure on employers to nudge their employees into retirement accounts?

Everything in our online life is indexed. Every idle tweet, status update, or curious search query feeds the Google database. The tech giant recently bought a leading artificial-intelligence research outlet, and it already has a robotics company on its books. So what if Google, or Facebook, or any of the companies we entrust our information to, wanted to use our search histories to create an artificially intelligent robot? Writer and director Alex Garland's new film, Ex Machina, looks at just that.

Data scientists are no longer needed to mine data for business value, and organisations should instead look to machine learning platforms.

As I discussed in my previous blogs, artificial intelligence in the form of cognitive computing, robotic processing automation and other technologies presents insurers with many new opportunities for efficiency and for accelerated growth. The question for insurers is whether to use AI to automate processes or to augment the workforce and make it more creative and effective. Many insurers are adapting AI to automate basic underwriting transactions and renewal processing. AI also provides consistent, low-cost performance in making underwriting eligibility decisions. Insurers are exploring the use of AI to augment the expert workforce in areas including risk management, client and/or prospect discovery, coverage recommendations and fraud detection.