There are 40 million people in slavery today. SDG 8.7 is a commitment to end modern slavery, with the ambition to reduce that number by 10,000 people every day. James Cockayne, Director, Centre for Policy Research, United Nations University, is confident that we are "nowhere near" that target. The reasons for this systemic and enduring failure are the result of the "mispricing" of labour, where true social costs are not quantified. Worse still, companies are rewarded for driving down their labour costs.

A recent study by IBM Research, together with Sage Bionetworks, Kaiser Permanente Washington Health Research Institute, and the University of Washington School of Medicine, has uncovered how combining machine learning algorithms and assessments by radiologists could improve the overall accuracy of breast cancer screenings. Mammogram screenings, commonly used by radiologists for the early detection of breast cancer, according to IBM researcher Stefan Harrer, frequently rely on a radiologist's expertise to visually identify signs of cancer, which is not always accurate. "Through the current state of human interpretation of mammography images, two things happen: Misdiagnosis in terms of missing the cancer and also diagnosing cancer when it's not there," Harrer told ZDNet. "Both cases are highly undesirable -- you never want to miss a cancer when it's there, but also if you're diagnosing a cancer and it's not there, it creates enormous pressure on patients, on the healthcare system, that could be avoided. "That is exactly where we aim to improve things through the incorporation of AI (artificial intelligence) to decrease the rate of false positives, which is the diagnosis of cancer, and also to decrease missing the cancer when there is one." The research used more than 310,800 de-identified mammograms and clinical data from Kaiser Permanente Washington (KPWA) and the Karolinska Institute (KI) in Sweden. Of the combined datasets, KI contributed around 166,500 examinations from 6,800 women, of which 780 were cancer positive; while the remaining 144,200 examinations were provided by KPWA from 85,500 women, of which 941 were cancer positive. "We had hundreds of thousands of mammograms that were annotated.

FinTech breaks down some of the key technologies behind the digital transformation of the insurance industry, and explores the startups behind their rapid adoption. As the technological transformation of the global business landscape continues, more and more industries are feeling the pressure to embrace the potential of digital solutions, or be consigned to the scrapheap. In the unending quest for legacy industries to stay agile and avoid disruption, leading edge technologies are fusing with existing business models to create new, tech-driven disciplines. Fintech has been one of the great success stories of this trend, as financial institutions harness the power of artificial intelligence (AI), big data and multi-platform customer experiences (CX) to meet the expectations of their customers. One subset of the fintech space that's proven to be ripe for innovation and disruption in the past few years is the insurance industry. As one of the oldest financial businesses, insurance has traditionally been a market that's slow to adopt, cautious and favours those with deep pockets who already have a seat at the table.

Inthe connected world of today, the role of a user has changed from a passive spectator to an active participant. With millennials spending close to 9.6 hours per day and Gen X spending 7.9 hours per day, on average, consuming digital content, attention has become a precious commodity in this digital age. This is happening because we are surrounded with personalized interactions, personalized campaigns, personalized news feeds and personalized deliveries due to AI-powered data driven tools. Artificial Intelligence (AI) has altered the basic idea of how consumers perceive a product and the way marketers perceive the purchase journey of customers. AI-driven personalization technology is able to accurately determine the consumer journey and can anticipate the next step along that journey.

Adriverless car running on roads may sound like a screen taken from a sci-fi movie. However, fiction is turning into reality, and we thank Artificial Intelligence (AI) for the same. AI technology complements the very concept of self-driving cars. Elon Musk had predicted in 2017 that all the cars will be autonomous in 10 years without any steering wheel. We are quite close to bringing this prediction into reality in just a short time frame of 4 years.

UiPath is a Robotic Process Automation tool that is used for Windows desktop automation. It is used to automate repetitive/redundant tasks with the help of drag and drop functionality and eliminates human intervention. This tool offers various editions to support different types of users and comes with an active community to resolve issues.

Global tech giant Fujifilm has given Welsh healthcare professionals an exclusive first look at its latest medical innovation ahead of the product's global clinical launch. The Japanese multinational photography and imaging company, which is a pioneer in medical imaging and diagnostics equipment, previewed its new artificial intelligence (AI) software, which is integrated into a mobile radiography system, at an event hosted by Life Sciences Hub Wales. Flown in from Tokyo for the event, the FDR nano is a mobile X-ray unit that uses integrated AI technology to quickly identify and flag abnormalities that need further investigation. The product is the first Fujifilm AI-enabled mobile unit in Europe and is due to commence clinical trials in a UK hospital. The AI in the unit highlights suspicious areas on an image to the radiographer taking the X-ray using a heat map.

A field that is bringing alot of commotion and noise is Artificial Intelligence. But something that really fascinates me is a subset of that field known as Artificial General Intelligence (AGI) or the holy grail of Artificial Intelligence. Many of today's machine learning or deep learning algorithms would be classified as Artificial Narrow Intelligence (ANI). I believe many of these algorithms are rapidly proliferating at the back end of most technologies we currently use from ride-sharing apps to social media and to other applications. And I believe that will continue to happen at an exponential pace until many specific tasks can be done better by algorithms than by humans.

IBM is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights. The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyze idioms and colloquialisms for the first time. Phrases, like'hardly helpful,' or'hot under the collar,' have been challenging for AI systems because they are difficult for algorithms to spot. With advanced sentiment analysis, businesses can begin analyzing such language data with Watson APIs for a more holistic understanding of their operation. Further, IBM is bringing technology from IBM Research for understanding business documents, such as PDF's and contracts, to also add to their AI models.

Chico Caramago, a postdoctoral researcher in data science at the Oxford Internet Institute came to data science from a background in biology. "Biology is big, messy and complex," he told Built In, "so I was drawn toward tools that could help me make some sense out of that." Usually, humans make sense of the natural world's complexity with our own natural tools: our brains and our senses. Caramago was especially drawn to unsupervised machine learning and natural language processing, which helps humans with everything from detecting signs of metastasizing cancer to understanding foreign languages with Google Translate. At this point, in fact, data science has gotten so sophisticated that it doesn't just enhance our natural abilities -- it mimics them. Take deep learning, for example.