Census data that shows that 231 million Americans speak only English at home and do not know another language well enough to communicate in it. But how can you learn a new language without going back to school? Machine learning could be a solution to this problem, by cutting down on the 200 hours it takes to learn a language using traditional methods. Language company Lingvist intends to decrease this time by using machine learning software to adapt to your learning style. The algorithm certainly seems to work well -- and the way certain words are reinforced makes sure that they stick in your mind.
I become addicted to learning a new language with the Lingvist language software within a day of using it. Census data that shows that 231 million Americans speak only English at home and do not know another language well enough to communicate in it. But how can you learn a new language without going back to school? Machine learning could be a solution to this problem, by cutting down on the 200 hours it takes to learn a language using traditional methods. Language company Lingvist intends to decrease this time by using machine learning software to adapt to your learning style.
We present a deep neural-network model for lifelong learning inspired by several forms of neuroplasticity. The neural network develops continuously in response to signals from the environment. In the beginning, the network is a blank slate with no nodes at all. It develops according to four rules: (i) expansion, which adds new nodes to memorize new input combinations; (ii) generalization, which adds new nodes that generalize from existing ones; (iii) forgetting, which removes nodes that are of relatively little use; and (iv) backpropagation, which fine-tunes the network parameters. We analyze the model from the perspective of accuracy, energy efficiency, and versatility and compare it to other network models, finding better performance in several cases.
The technology sector is set to benefit from a £18.5 million cash injection to drive up skills in AI and data science and support more adults to upskill and retrain to progress in their careers or find new employment. Up to 2,500 people will have the opportunity to retrain and become experts in data science and artificial intelligence (AI), thanks to a £13.5 million investment to fund new degree and Masters conversion courses and scholarships at UK academic institutions over the next three years. The ground-breaking Adult Learning Technology Innovation Fund, which will be launched in partnership with innovation foundation Nesta, will provide funding and expertise to incentivise tech firms to harness new technologies to develop bespoke, flexible, inclusive, and engaging online training opportunities to support more people into skilled employment. Companies across the tech sector already employ more than 2.1 million people, contribute £184 billion to the economy every year and inward investment to the UK AI sector stood at £1 billion for 2018, which is more than Germany, France, Netherlands, Sweden and Switzerland combined. To further strengthen the sector, Government is investing in data-driven technologies, such as artificial intelligence, through the modern Industrial Strategy, so tech businesses and people with the drive and talent can succeed.
The government has announced an investment of up to £18.5m to support efforts to enhance diversity in artificial intelligence (AI) and data science roles. Part of a wider plan to upskill the UK workforce as part of the AI Sector Deal to position the country as a leader in use of the technology, £13.5m of the total funding will go towards up to 2,500 AI and data science conversion courses for professionals who have degrees in other disciplines, as well as 1,000 scholarships. The programmes will aim to support applications from professionals returning from a career break and looking to retrain, as well as under-represented groups in the digital workforce, including women and those from minority ethnic or lower socio-economic backgrounds. Around £5m will be invested into the Adult Learning Technology Innovation Fund, to be launched in partnership with innovation foundation Nesta, which will seek to encourage companies to use AI and automation to improve online learning platforms aimed at helping adults retrain. "The UK has a long-standing reputation for innovation, world-leading academic institutions and a business-friendly environment. Everyone, regardless of their background, should have the opportunity to build a successful career in our world-leading tech sector," said digital secretary Jeremy Wright.
Mark Cuban is not content resting on his laurels as a billionaire entrepreneur. Cuban often describes himself as a voracious reader who believes that "life-long learning is probably the greatest skill" a successful person can have. What's more, the Dallas Mavericks owner and star of ABC's "Shark Tank " says he never wants to stop reading and learning, because it makes him a better investor, especially in the tech space, which is always evolving. "I've been on Amazon doing the machine learning tutorials," Cuban recently told Yahoo Finance. Cuban said he has been learning how to build neural networks, which are computer algorithms modeled after the brain that are used in artificial intelligence, as well as taking online computer coding classes to learn the latest versions of programming language Python.
In many real-world sequential decision making problems, the number of available actions (decisions) can vary over time. While problems like catastrophic forgetting, changing transition dynamics, changing rewards functions, etc. have been well-studied in the lifelong learning literature, the setting where the action set changes remains unaddressed. In this paper, we present an algorithm that autonomously adapts to an action set whose size changes over time. To tackle this open problem, we break it into two problems that can be solved iteratively: inferring the underlying, unknown, structure in the space of actions and optimizing a policy that leverages this structure. We demonstrate the efficiency of this approach on large-scale real-world lifelong learning problems.
Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Na\"ive Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focuses on how to accumulate knowledge during learning and leverage them for further tasks. Meanwhile, the demand for labelled data for training also is significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labelled data and computational cost to achieve the performance as well as or even better than the supervised learning.
Worried about the impact technology is having on your job? Stay curious and keep learning. That, in essence, is the message delivered by Google's country director for Canada, Sabrina Geremia, to participants on the last day of the C2 Montreal conference. "Disruption anxiety is in every single field," Geremia said Friday during an exchange with Jui Ramaprasad, a professor of information systems at McGill University. "Part of the anxiety is rooted in the pace of change. Right now, your life is the slowest it's ever going to be." Google's own statistics provide insights into the increasing speed of change.
His company invests in new technology like AI to make businesses more efficient -- but, he wondered, what was AI doing to the people whose jobs might change, go away or become less fulfilling? The question sent him on a two-year research odyssey to discover what motivates people, and why we work. In this conversation with curator Bryn Freedman, he shares what he learned, including some surprising insights that will shape the conversation about the future of our jobs.