Artificial intelligence is nothing new. Researchers have been trying to solve the puzzle of creating human-like intelligence in machines for decades. But it's only in recent years that they've started to make real progress towards realizing this incredible goal. Just in the last few years we've seen the emergence of chatbots, self-driving cars, smart appliances, and smarter search engines. They all rely heavily on artificial intelligence.

A group of academics and technology experts have embarked on a study to find out how life will be in 2030 with the huge developments in Artificial Intelligence. Although these advances in technology will create some new challenges, however it looks like that there will be a lot more of benefits to gain from. The report entitled "Artificial Intelligence and Life in 2030" forms part of The Stanford University's One Hundred Year Study on Artificial Intelligence. It is the first part from a series which will be published at regular intervals. In this study, experts focused on studying eight particular sectors which are most likely to be affected by Artificial Intelligence by the year 2030.

Where Google is moving towards lifelike AI, Hiroshi Ishiguro's Alter is moving towards embracing the inhuman. Google's AI can dream up its own surreal images, conversation, and beat a human champion at the ancient game of Go. Now it can realistically mimic human speech, including the non-speech sounds the mouth and respiratory system make when a human talks. The system is called WaveNet, a neural network that generates raw audio waveforms, and it's uncannily lifelike. We do have text-to-speech generators, and they're very useful, particularly for blind people.

Call me optimistic, I'm a fan of the strong robot economy, where large-scale robot automation and artificial intelligence teams up to deliver us from evil and bring about a comparative paradise. But there will be pain, and not everyone is convinced the pain will be temporary. While science fiction has focused on the existential threat of AI to humans, researchers at Google's parent company, Alphabet, and those from Amazon.com, Facebook, IBM and Microsoft have been meeting to discuss more tangible issues, such as the impact of AI on jobs, transportation and even warfare. In recent years, however, the AI field has made rapid advances in areas, from self-driving cars and machines that understand speech, like Amazon's Echo device, to a new generation of weapons systems that threaten to automate combat.

Microprocessor and chip maker NVIDIA is using artificial intelligence onboard its newest computer system that will power Chinese web company Baidu's self-driving car, according to a Reuters report. Baidu is the Chinese equivalent of Google, and operates the country's most popular search engine of the same name that holds a 56.3% market share. NVIDIA, as some of us know, is famous for its Graphics Processing Units (GPUs) that powers many high-end gaming PCs and laptops, as well as super-fast mobile processors like the Tegra series. But NVIDIA has been eyeing the artificial intelligence space for quite some time now. Because AI requires a very high level of computational power.

Artificial intelligence (AI) has gradually become an integral part of modern life, from Siri and Spotify's personalized features on our phones to automatic fraud alerts from our banks whenever a transaction appears suspicious. Defined simply, a computer with AI is able to respond to its environment by learning on its own--without humans providing specific instructions. A new report from Stanford University in Palo Alto, California, outlines how AI could become more integrated into people's lives by 2030, and recommends how best to regulate it and make sure its benefits are shared equally. Here are five examples--some from this report--of AI technology that could become a part of our lives by 2030. Smart traffic lights using artificial intelligence technology to learn and adapt to traffic patterns in real time could make intersections safer and more efficient.

A Japanese startup is entering the equity research business in a bid to challenge the dominance of securities firms by using computers to crunch vast troves of information and predict companies' earnings. Nowcast Inc., a financial-technology venture formed last year out of the University of Tokyo, will begin providing automated earnings estimates of consumer goods makers as soon as October by analyzing millions of transactions at retail stores, Chief Executive Officer Ryota Hayashi said. The move comes as pressure from Japan's financial regulator prompts brokerages to move away from a long-standing practice of gleaning information from companies about their performance before earnings figures are released. Hayashi sees this as an opportunity for Nowcast to find other ways to estimate companies' results and sell the research to active investors such as hedge funds. "This doesn't mean analysts won't be needed anymore," Hayashi said in an interview.

Machine learning is a subfield of computer science and a set of algorithms focused on patter recognition and computational learning. Through machine learning a computer program can improve its accuracy without being explicitly program to. Such a computer program will learn from experience and train itself in fulfilling better its tasks. Deep learning is a type of machine learning where artificial neural networks with many layers are used. Artificial neural networks mimic in a highly abstracted way the human brain.

WWDC16 just ended and Apple left us with new amazing innovative APIs. This year speech recognition, proactive applications, machine learning, user intents, and neural networks have been the most frequent terms used during the conference. So, besides a new rich version 3 of Swift, almost every new addition to iOS, tvOS and macOS is related with artificial intelligence. For example, Metal and Accelerate in iOS 10 provide an implementation of convolutional neural networks (CNNs) for the GPU and CPU respectively. During the keynote, Craig Federighi (Apple's SVP Software Engineering) showed how the Photos app on iOS organizes our photos according to different smart criteria. He highlighted that Photos app uses deep learning to provide such functionality. Also, Federighi showed how Siri, now available to developers, can suggest what we need.

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