CORPORATE BOARDS are besotted with artificial intelligence. Worldwide spending on AI is expected to rise from $38bn this year to $98bn by 2023, estimates IDC, a research firm. So far, though, only one in five companies aware of the technology's potential has incorporated machine learning into its core business. One reason for the slow uptake is the dearth of quality data to teach algorithms to perform useful tasks. The most common form of AI, called "supervised learning", requires feeding software stacks of pre-tagged examples of, say, cat pictures until it can tell a feline image apart by itself.

The UK's Department for Work and Pensions (DWP) is exploring the use of AI to judge whether benefit claims are true. An £8 million'intelligent automation garage' has been created by the DWP with the goal of developing up to 100 new robots to assist Britain's welfare system. The DWP is working with New York-based UiPath on the system, along with tech giants IBM, Tata Consultancy, and Capgemini. UiPath's systems are already being used by notable brands such as Walmart, Toyota, and several banks. Ultimately, the DWP is billing the new AI system as simplifying the welfare system in order to process and issue benefits payments faster.

This article was first published on the Impact Money Blog. The Impact Money Blog sat down with Puneet Gupta to find out why he left Dvara Trust (formerly known as IFMR Trust), a group he co-founded in 2007, to start Kaleidofin, an ambitious fintech company that wants to make savings and investment convenient for millions of customers at the base of the economic pyramid. Puneet Gupta, a veteran social entrepreneur in the field of microfinance, knows the poor would be better off saving rather than borrowing to achieve their financial goals. Although microfinance has been credited with improving the lives of hundreds of millions of people in the developing world, the industry has grappled with the ethics of indebting the most economically vulnerable. This is particularly true in India, which experienced a regulatory backlash over this issue in 2010 that threw the industry into turmoil.

We all remember the painful arithmetic exercises at school. It takes at least a minute to multiply numbers like 3,752 and 6,901 with pencil and paper. Of course, today, when we have phones at hand, we can quickly check that the result of our exercise should be 25 892 552. Processors of modern phones can perform more than 100 billion of such operations per second. Moreover, these chips consume only a few watts, which makes them much more efficient than our slower brains, which consume 20 watts and require much more time to achieve the same result. Of course, the brain has not evolved to do arithmetic.

The technological singularity, an age when machine intelligence surpasses human intelligence, is now expected to take place in 2035, 10 years earlier than initially predicted. This was the word from Shayne Manne, co-CEO of SingularityU Africa and co-founder of experiential brand agency Mann Made. Almost 2 000 attendees filled the conference centre at the Kyalami Grand Prix Circuit at the SingularityU South Africa Summit 2019 yesterday. Manne, who delivered the welcome note, discussed the current exponential change and possibilities presented by technology. He explained that singularity,a hypothetical point in the future when technological growth and machine intelligence become uncontrollable and irreversible, once predicted to take place in 2045, is now expected to take place a decade earlier, and is anticipated to result in unfathomable changes to human civilisation.

MDOTM, a provider in quantitative investment advisory services to institutional investors, and Raiffeisen Capital Management have announced a new strategic partnership. Thanks to this initiative, the range of Raiffeisen Capital Management's sustainable funds will be used by MDOTM to offer to the market socially responsible investment (SRI) solutions that benefit from the efficiency brought by artificial intelligence (AI) technology in portfolio construction. Raiffeisen Capital Management's offering in the sustainable investment segment comprises eight investment funds with different risk/return profiles: Sustainable Balanced, GreenBonds, Diversified Sustainable, Sustainable Solidity, Sustainable Emerging Markets, Sustainable Short Term, Sustainable Momentum and Sustainable Equity. MDOTM is a fintech company that develops investment strategies for financial institutions with the help of AI, machine learning and advanced statistical methods. The startup acts as an advisor to banks, wealth managers, asset management companies, and insurance companies by supporting them in specific areas which require high degree of technological specialisation by providing them with systematic models for investment decision making.

It is a truism that artificial intelligence (AI) is set to change the world in unimaginable ways. The giants of the tech industry have realised it and are investing heavily in it, as can be seen, for instance, from Microsoft's $1 billion investment in OpenAI, which in turn was founded by Tesla's Elon Musk, and seeks to use AI to benefit all of mankind. Again, Twitter has acquired four AI companies - the biggest of them being Magic Pony for $150 million in 2016 - in its bid to improve its system of recommending specific tweets in users' timelines. Even traditional businesses are using AI to improve their services, such as UK-based grocer Nisa Retail employing Amazon Web Services to meet its business challenges. India too has plunged headlong into AI and machine learning (ML) with numerous start-ups offering AI solutions in areas such as banking, logistics and transportation.

In this video from the HPC User Forum at Argonne, Ian Colle from Amazon presents: What Can HPC on AWS Do? AWS provides the most elastic and scalable cloud infrastructure to run your HPC applications. With virtually unlimited capacity, engineers, researchers, and HPC system owners can innovate beyond the limitations of on-premises HPC infrastructure. AWS delivers an integrated suite of services that provides everything needed to quickly and easily build and manage HPC clusters in the cloud to run the most compute intensive workloads across various industry verticals. These workloads span the traditional HPC applications, like genomics, computational chemistry, financial risk modeling, computer aided engineering, weather prediction, and seismic imaging, as well as emerging applications, like machine learning, deep learning, and autonomous driving." Ian Colle joined AWS as the General Manager for AWS Batch and HPC in November 2017.

In a media briefing at Dell's AI Experience Zone in Singapore, Dell Technologies announced that AI Singapore has chosen Dell Technologies to deliver High-Performance Computing (HPC) infrastructure that's optimised for AI workloads. AI Singapore, first announced in 2017, is a national program office launched by the National Research Foundation (NRF) to drive the adoption of artificial intelligence, develop the country's AI talent and help seed high-quality research efforts to develop fundamental AI novel techniques, algorithms and adjacent technologies. In the collaboration, Dell Technologies will provide three key computational building blocks for the new supercomputer at AI Singapore to help drive performance and flexibility for its researchers and to scale up its flagship 100 Experiments (100E) program. According to Laurence Liew, Director, AI Industry Innovation, for the 100E program, AI Singapore would partner with companies or industries that need AI solutions, but there are no commercially available solutions available for them in the market, or when they're committed to building their own products to compete globally. "The way we support them is by bringing our professors, researchers and engineering teams to work together with the companies to build their AI products and solutions," he explained.

Machine learning has the potential to support aspects of teaching and learning that are currently time consuming and difficult to manage, such as individual project work, collaboration, tutorials and self-directed learning. In 2018, the global Machine Learning in Education market size was xx million US$ and it is expected to reach xx million US$ by the end of 2025, with a CAGR of xx% during 2019-2025. This report focuses on the global Machine Learning in Education status, future forecast, growth opportunity, key market and key players. The study objectives are to present the Machine Learning in Education development in United States, Europe and China. The key players covered in this study IBM Microsoft Google Amazon Cognizan Pearson Bridge-U DreamBox Learning Fishtree Jellynote Quantum Adaptive Learning Market segment by Type, the product can be split into Cloud-Based On-Premise Market segment by Application, split into Intelligent Tutoring Systems Virtual Facilitators Content Delivery Systems Interactive Websites Others Market segment by Regions/Countries, this report covers United States Europe China Japan Southeast Asia India Central & South America The study objectives of this report are: To analyze global Machine Learning in Education status, future forecast, growth opportunity, key market and key players.