FUKUOKA: International Monetary Fund Managing Director Christine Lagarde warned on Saturday that the increasing presence of technology giants using big data and artificial intelligence could cause a significant disruption to the world's financial system. The rapid development of financial technology (fintech) has increased access to cheap payment and settlement systems for low-income households in emerging countries where traditional banking networks are scarce. But it has raised concern about the increasing dominance of big technology firms in mobile payments, which could force global policymakers to rethink the way they regulate the banking system and ensure financial settlements are executed safely. "A significant disruption to the financial landscape is likely to come from the big tech firms, who will use their enormous customer bases and deep pockets to offer financial products based on big data and artificial intelligence," Lagarde told a symposium on financial technology held on the sidelines of the G20 finance leaders' meeting in Fukuoka, southern Japan. While such innovation may help modernize financial markets, they could make the financial system vulnerable such by putting payment and settlement systems under the control of a handful of technology giants, she added.

In their work to unearth evidence of fraudulent activities, forensic accounting investigators dig through diverse data looking for anomalies that suggest something is just not right. But as the massive volumes of data collected by companies balloon, this task has become increasingly arduous, time-consuming and humanly impossible. Instead of investigators manually reviewing spreadsheet rows and columns, looking for three or four data elements that together indicate a suspicious transaction, ML can peruse thousands of data elements -- instantly. The regrettable consequence is the greater chance of a well-thought-out scam slipping through the cracks. A case in point is healthcare fraud, which has been estimated to cost the United States tens of billions of dollars annually.

The spread of tuberculosis (TB) has diminished in the developed world, but it is still prevalent in the developing parts of the world such as in Asia and Africa. The rise of HIV in the 1980's also saw an increase in TB infections due to the weakened immune systems of patients with HIV. Currently about 1.6 million people die from TB each year, and 10 million people develop active TB infections, which is also contagious. Tuberculosis is caused by Mycobacterium tuberculosis bacteria and it generally affects the lungs. Individuals can harbor the TB bacteria but show no symptoms.

That is the aim of this Boston-area startup. This year, it expects to come to market with technology to make even fast and powerful industrial robots safe to approach. The technology promises to eliminate the need for guarding around them -- safety measures that might not be as safe as you think. Automated manufacturing facilities are full of physical barriers -- guarding and fences -- because of our fears. That is, because of our entirely legitimate fears. Automated systems, industrial robots in particular, are capable of moving fast and unexpectedly, imperiling a human who comes too close.

In his session at 23rd International CloudEXPO, Raju Shreewastava, founder of Big Data Trunk, will provide a fun and simple way to introduce Machine Leaning to anyone and everyone. Together we will solve a machine learning problem and find an easy way to be able to do machine learning without even coding. He solved a machine learning problem and demonstrated an easy way to be able to do machine learning without even coding. Speaker Bio Raju Shreewastava is the founder of Big Data Trunk (www.BigDataTrunk.com), a Big Data Training and consulting firm with offices in the United States. He previously led the data warehouse/business intelligence and Big Data teams at Autodesk.

Mobile developers have a great deal to pick up from progressive changes that on-device ML can offer. This is a result of the innovation's capacity to reinforce mobile applications--in particular, taking into consideration smoother customer experiences equipped for utilizing incredible highlights, for example, giving exact area-based recommendations or promptly detecting plant illnesses. This fast improvement of mobile Machine learning has occurred as a response to various basic issues that traditional Machine Learning has toiled with. In truth, the composing is on the divider. Future mobile applications will require faster preparing paces and lower latency.

In the 2012 sci-fi film'Prometheus', a robot is seen performing surgeries albeit without human control. While that may be a bit far-fetched -- as reel life is -- Artificial Intelligence (AI) in healthcare is here to stay -- whether we like it or not. AI is making inroads into healthcare like never before with a promise to make healthcare faster, accessible to everyone and cut costs. Cut to India, and the health challenges are many and diverse. There is apalpable human resource shortage and often, healthcare does not reach remote areas.

International Monetary Fund Managing Director Christine Lagarde warned on Saturday that the increasing presence of technology giants using big data and artificial intelligence could cause a significant disruption to the world's financial system. The rapid development of financial technology (fintech) has increased access to cheap payment and settlement systems for low-income households in emerging countries where traditional banking networks are scarce. But it has raised concern about the increasing dominance of big technology firms in mobile payments, which could force global policymakers to rethink the way they regulate the banking system and ensure financial settlements are executed safely. "A significant disruption to the financial landscape is likely to come from the big tech firms, who will use their enormous customer bases and deep pockets to offer financial products based on big data and artificial intelligence," Lagarde told a symposium on financial technology held on the sidelines of the G20 finance leaders' meeting in Fukuoka, southern Japan.

What can we do about algorithmic bias? If you're a software developer or data scientist, IBM Research has an open source toolkit that helps you check bias in your data models. But it's not just technologists who can do something about algorithmic bias. You can start reclaiming digital space by exploring your choice in technology services. For example, by using search engines like DuckDuckGo, because unlike the voracious data vampire that is Google, it doesn't store your personal information to then use for targeted ads. You can also petition and lobby your government to adopt a governance framework for algorithmic accountability and transparency policy where "Algorithmic literacy" is introduced into curricular, and standardised notifications (to communicate type and degree of algorithmic processing in decisions) are made a requirement. Ultimately, we need to ask more of ourselves and tech companies. It's not enough to just employ critical thinking – we also need to employ civic thinking in how we build and use these technologies. This article first appeared in The Daily Maverick.

What if instead of political parties, presidents, prime ministers, kings, queens, armies, autocrats, and who knows what else, we turned everything over to expert systems? What if we engineered them to be faithful, for example, to one simple principle: "human beings regardless of age, gender, race, origin, religion, location, intelligence, income or wealth, should be treated equally, fairly and consistently"? Here's some dialogue – enabled by natural language processing (NLP) – with an expert system named "Decider" that operates from that single principle (you can imagine how it might behave if the principle was completely different – the opposite of equal and fair). The principle is supported by the data and probabilities the system collects and interprets. The "inferences" made by Decider are pre-programmed.