Researchers have devised a machine learning algorithm that looks at photos of food and predicts the recipe that created the dish. The AI can also work out from a photo what ingredients went into a food: presented with an image of a plate of biscuits, for example, it knows that they are likely to include flour, eggs and butter. App such as MyFitnessPal already let people track calorie intake, but they have to manually input what they eat. But image recognition algorithms can only go so far, says Christoph Trattner at MODUL University Vienna in Austria.
So when a machine takes decisions like an experienced human being in similarly tough situations are taken by a machine it is called artificial intelligence. You can say that machine learning is a part of artificial intelligence because it works on similar patterns of artificial intelligence. Finally in the 21st century after successful application of machine learning artificial intelligence came back in the boom. As machine learning is giving results by analyzing large data, we can assure that it is correct and useful and time required is very less.
Erik Brynjolfsson (@erikbryn) is the director of MIT's Initiative on the Digital Economy, the Schussel Family Professor of Management Science at the MIT Sloan School of Management, and a research associate at NBER. His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. Brynjolfsson is the author of several books, including, with Andrew McAfee, Machine, Platform, Crowd: Harnessing Our Digital Future (2017) and the New York Times best seller The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (2014). With Erik Brynjolfsson he coauthored Machine, Platform, Crowd: Harnessing Our Digital Future (2017) and The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (2014), which was a New York Times best seller and was shortlisted for the Financial Times/McKinsey Business Book of the Year Award.
Basic machine learning algorithms underpin many technologies that we interact with in our everyday lives - voice recognition, face recognition - but are application-specific and can only do one very specific defined task (and not always well). More capable AI - what we might consider as being somewhat smart - is only now becoming widespread in areas such as online retail and marketing, smartphones, assistive car systems and service robots such as robotic vacuum cleaners. Most recently, Google's DeepMind AI called AlphaGo beat the world champion Go player, surprising a lot of people – especially since Go is an extremely complex game, way surpassing chess. First, there is a long runway of steady incremental improvements left in many areas of conventional AI - large, complex neural networks and algorithms.
However, this is just the beginning: with companies such as Google, Microsoft and Facebook spending millions on research into advanced neural networks and deep machine learning, computers are set to get smarter still. "There's a good reason why Google remains at the forefront of the deep learning revolution: data, and lots of it." Although Apple has recently been on a hiring mission, seeking 80-plus AI experts to help make Siri smarter than Google Now or Microsoft's Cortana, it's still playing catch-up. Google is using the lessons it's learnt in image recognition to advance a whole gamut of technologies including speech recognition, Street View detection, language translation and spam detection.
Imagine a recruiter can watch a video of your face and analyse your facial expressions. Face Recognition software is taking the world by storm. In Europe, a number of high-end hotels and retailers are reportedly using facial recognition to help identify VIPs and celebrities for preferred treatment when they enter the front door. Obviously, adding face recognition technology to this video platform could be of tremendous benefit to an employer.
A big construction project is like a celebrity. This may be the initial plan at many construction sites, but the sheer amount of video or pictures taken on a big construction project can quickly get out of hand. Deep learning is used by Smartvid.io A compute-intensive operation, deep learning requires some serious horsepower--of the kind offered by banks of GPUs. Along with the undisclosed financial investment Autodesk has made, Smartvid.io Autodesk had been making its own stab at mining construction project data using machine learning and analytics with its Project IQ, although the data it was mining does not appear to include video.
To build and run machine learning services you need computing power and data, and the more you have of each the more powerful your software can be. Image recognition is particularly good on mobile devices, says Song Han, a Stanford University graduate student working on compressing neural networks. He developed one such system that helps Facebook's augmented reality platform track objects. And Qualcomm, the leading chipmaker for Android devices, has been working on hardware tricks to speed up neural networks on mobile devices for some time.
Computer vision startup Clarifai has launched a mobile software development kit (SDK) in limited preview today to process and carry out artificial intelligence on iOS devices. The news is significant because it allows mobile users to carry out AI computations -- even on their iPhone without a connection to the cloud, which is typically how machine learning is handled on mobile devices today. Though no internet connection is necessary for the SDK to operate, use of Clarifai in the cloud will allow a user to synchronize or share their AI trained to recognize specific objects, faces, or pets. Clarifai hopes its computer vision models combined with the mobile SDK will be adopted to tackle use cases in many industry verticals, but one early partner already using the mobile SDK is medical imaging company i-Nside.
To equip AI to deal with the real world, IBM challenged its computer and data scientists to create a program that could defeat human contestants at Jeopardy!, a quiz show requiring answers to natural language questions over broad domains of knowledge otherwise known as unstructured data. A single question, for example, can generate 100 answer candidates, each with 100 evidence sources, and each scored by 100 algorithms. In the span of five years, image recognition, speech recognition, speech synthesis, machine translation, drug discovery, and robotics all have reached new levels of performance thanks to deep learning. A well-known question answering benchmark is TREC QA, Text REtrieval Conference Question Answering, which is approaching 80% accuracy, as shown below, thanks to deep learning based techniques which process text.