Apple co-founder Steve Wozniak has claimed that computers with human like intelligence were not so far as everyone thought they were. Speaking during a business meet in Australia, Wozniak predicted that in his lifetime he would see computers with artificial intelligence. He said to a bewildered crowd that humans had set to create machines that make their jobs easier adding that mankind strives to come up with technology does a particular job instead of them doing it. "Every time we create new technology we're creating stuff to do the work we used to do and we're making ourselves less meaningful, less relevant," Wozniak said. "Why are we going to need ourselves so much in the future? We're just going to have the easy life," he added.

After spending decades in the shadows as a specialty discipline, machine learning is suddenly front and center as a business tool. The hard part, though, is making it useful, especially to the developers and budding data scientists who are being tasked with the job. To that end, we rounded up some of the most common and useful open source machine learning tools we've spotted in the wild. For Python: Data scientists have jumped on Python as a more open-ended alternative to analytical languages like R, and many employers looking to add big-data expertise to their rosters are listing Python as a desired skill. As a result, plenty of machine learning libraries have shown up in Python's ever-expanding software roster.

Some of the first pieces of common sense a computer ever learned, like the fact that helicopters are found on airfields and babies have eyes, are now available for the public to see online. Nobody had to tell the Never Ending Image Learner (NEIL) these everyday facts - the computer program figured them out for itself by analysing millions of images from the web all day every day for the last four months. NEIL uses recent advances in computer vision that allows programs to identify and label objects within images and then recognise elements of them - like colours, lighting and materials - all with the minimum of human supervision. But the project takes things a bit further by trying to make associations between the pictures it sees to come up with everyday rules, such as that cars are often found on roads or that ducks (sort of) look like geese. Abhinav Gupta, lead investigator and assistant research professor at Carnegie Mellon University's Robotics Institute, where NEIL spends its time, said that images were the best way to learn visual properties.

Dr Milne, who has already climbed Carstensz Pyramid (Oceania) Vinson Massif (Antarctica), Elbrus (Europe), Kilimanjaro (Africa), Denali (North America) and Aconcagua (South America), will be the first mountaineer to use the IM-PACs (intelligent messaging, planning and collaboration) system. The technology, developed at the Artificial Intelligence Applications Institute in the University of Edinburgh's School of Informatics, has been designed to provide computer support to people and teams performing a range of tasks - not just expedition teams operating in extreme conditions, but also key personnel involved in planning and rescue services responding rapidly to emergencies. IM-PACs' foundations in artificial intelligence planning technologies supply a framework that encourages a methodological approach to any task and allows users to transmit and respond to information in ways that can adapt to the circumstances the expedition team finds itself in. During his ascent, Dr. Milne will be in regular contact with colleagues in base camp who will monitor his progress against his ascent plan. A laptop computer and satellite phone will allow details of his current status and progress to be sent over the internet to a support team in Edinburgh.

GIVEN the choice, who would you rather trust to safeguard your future: a bloodsucking lawyer or a cold, calculating computer? Granted, it's not much of a choice, since neither lawyers nor computers are renowned for their compassion. But it is a choice that you may well encounter in the not-too-distant future, as software based on "artificial intelligence" (AI) starts to dispense legal advice. Instead of paying a lawyer by the hour, you will have the option of consulting intelligent legal services via the web. While this might sound outlandish, experts believe that the advent of smart software capable of giving good, solid legal advice could revolutionise the legal profession.

WHEN it comes to the difficult problem of deciding who gets to keep the holiday home, the dog and the Barry Manilow albums, divorcing couples now have somewhere new to turn. Researchers in Australia have developed a computer program that relies on a branch of mathematics known as game theory to produce a fairer outcome when dividing property. Instead of the traditional approach of dividing a couple's property in half, the system, called Family Winner, guides the couple through a series of trade-offs and compensation strategies. According to John Zeleznikow, a computer scientist at Victoria University in Melbourne, who developed the software with his colleague Emilia Bellucci, the results are fairer because both parties end up with what they value most. The software was tested last year on 50 divorcing couples, with the outcomes evaluated by Victoria Legal Aid.

Artificial intelligence and automation technologies are already starting to affect our work and daily lives. AI is present in everyday objects and processes such as virtual assistants, supermarket checkouts, driverless cars and detecting fraud in credit card transactions. Disruption is inevitable but it is often deeply feared. The current wave of change, fuelled by technological advancement, is no different. However, like generations before us, we must learn to transcend the disruption and thrive in new times.

A new Twitter bot shows that not all them are bigots -some just want to make you smile. '@smilevector' was recently unleashed on Twitter and, unlike its Hitler supporting predecessor Tay, this bot manipulates faces by turning their frown upside down. Using neural networks, this algorithm can plant unsettling smiles on celebrities with what seems to be impressive accuracy. '@smilevector was unleashed on Twitter and, unlike its Hitler supporting processor Tay, this bot manipulates faces by making them smile. Created by Tom White, a lecturer at the Victoria University of Wellington School of Design in Wellington, New Zealand, this algorithm uses a generative neural net to manipulate faces.

In the year since the European Court of Justice ruled that anyone can ask Google to remove personal information about them, the site has evaluated more than one million links. Each request has to be verified and processed by a dedicated team of people, but the sheer volume can cause delays. To speed this up, researchers from Germany and New Zealand have developed an algorithm capable of analysing hundreds of such requests in seconds. Oblivion (illustrated) allows a user to automatically find and tag their personal information on the web, using both text - or natural language processing (NLP) - and image recognition. And they hope to offer it to Google, and other search engines, to help them manage future demands.

On screen an adult talks to a baby, showing images and words that the baby identifies and says aloud. But this isn't a parent talking to a child - rather, it's a researcher talking to an artificially intelligent human known as BabyX. The virtual infant learns and acts just like a real baby, and it could be an important step to creating artificially intelligent brains. Scientist at Auckland University have designed a virtual baby that learns. Called BabyX the computer programme can recognise words and images (shown).