Last year Microsoft and Google both showed that their image-recognition algorithms had learned to best humans. They independently created software that could exceed the average human score on a standard test that challenges software to recognize images of a thousand different objects, from mosques to mosquitoes. But to get good enough to defeat humanity, each company's software scrutinized 1.2 million labeled images. A child can learn to recognize a new kind of object or animal using only one example. Startup Geometric Intelligence said Monday that it has developed machine-learning software that is a much quicker study.
NVIDIA is hiring Machine Learning Framework software engineers for its GPU-accelerated Machine Learning team. Academic and commercial groups around the world are using GPUs to power a revolution in machine learning, enabling breakthroughs in problems from image classification to speech recognition to natural language processing. The group will be responsible for developing core deep learning algorithms for both internal and 3rd party codebases. Framework Software Engineers will be active members of the open source deep learning software engineering community, and will contribute directly to software packages such as Caffe, Theano, Torch, and KALDI.
The activities of many globally active IT corporations prove that machine learning will be high on their lists. Be it Google, IBM or Microsoft – all of them have made machine learning an important component of their business strategies. In addition, the tech giants have been recruiting entire competence teams and acquiring machine learning and AI startups. While IT, automotive, telecommunications and media are among the pioneers of this development, more traditional industries such as the chemicals sector, logistics/transportation and pharmaceuticals are already awaiting their turn. This makes me wonder whether machine learning can offer genuine value to the field of software development itself.
Can machine learning be used to accelerate the development of traditional software development lifecycle? As artificial intelligence and other techniques get increasingly deployed as key components of modern software systems, the hybridisation of AI and ML and the resultant software is inevitable. According to a research paper from the University of Gothenburg, AI and ML technologies are increasingly being componentised and can be more easily used and reused, even by non-experts. Recent breakthroughs in software engineering have helped AI capabilities to be effectively reused via RESTful APIs as automated cloud solutions.
Deep learning is a buzzword that has been hyped by the business and technical press for years, often with relatively meager results that failed to live up to expectations. But over the past 18 months, according to a new report from Tractica, the true power of deep learning has been realized, thanks to advances in hardware and algorithms that use pattern recognition applied in a continuous learning loop, enabling them to train themselves to perform tasks without requiring explicit programming code. The sheer power of deep learning, however, is likely to lead to the development of more powerful and disruptive applications of tomorrow, such as driverless cars, personalized education, and preventative healthcare. Tractica forecasts that, with this expanding set of applications, the worldwide deep learning software market will grow from $3 billion in 2017 to $67.2 billion by 2025. "Deep learning has been a key point of focus for many companies, given its potential to transform entire industries," says principal analyst Keith Kirkpatrick.