From depression chatbots to diagnosing eye diseases, developers are coming up with incredible ways of teaching robots to transform the African industries they work in. So says smart technology entrepreneur Jacques Ludik, founder of AI company Cortex Group and of the Machine Intelligence Institute of Africa. According to Ludik, artificial intelligence could be the powerful tool Africa needs to tackle its challenges, suck as shortcomings in health, education or employment. While many people see robots as a potential threat to jobs, Ludik believes AI holds the key to uplifting communities, through education and economic development. "Education is key to bridging the increasing gap of our experiences of the world, and improve and increase access to jobs (and other services) that aren't bound by geography. AI, the very technology that is moving the rest of the world forward so rapidly, has the power to do that."

Oblivious to those depending on bananas for their favourite protein shake or breakfast fix, a deadly fungus has sneaked up on banana plantations in South America, threatening the fruit's future. An artificial intelligence-powered smartphone app developed for banana farmers can come in handy to stem further spread of the disease, its makers from Colombia, India and U.S., said. The artificial intelligence-powered tool built into the app called Tumaini – which means "hope" in Swahili- can detect pathogens at an early stage and help fast-track control and mitigation efforts, according to a statement by researchers who designed the application. The app has been developed by scientists from the Colombia-based International Center for Tropical Agriculture (CIAT), the Imayam Institute of Agriculture and Technology (IIAT), Tamil Nadu in India, and Texas A&M University, in the United States. The tool is being tested on three main banana-producing continents: Asia, Latin America and Africa. It has been tried in Colombia in South America; the Democratic Republic of Congo, Benin and Uganda in Africa; and India and China in Asia.

Financial services providers that slack on regulatory compliance and fail to safeguard their operations against money laundering, terrorist financing and other criminal activities may face damaged reputations and significant fines. Compliance failures are prevalent worldwide: Approximately $26 billion worth of fines were levied against banks for AML, KYC and sanctions noncompliance between 2008 and 2018. A report found that the U.S. imposed a full $23.52 billion -- 91 percent -- of those penalties, while European regulators demanded $1.7 billion and the Middle East levied $9.5 million. FinTechs could face these same financial pains as regulators increasingly demand that they follow the compliance rules to which FIs must adhere. The People's Bank of China announced in March that it plans to create rules for regulating and securing the FinTech sector, for example.

Financial services providers that slack on regulatory compliance and fail to safeguard their operations against money laundering, terrorist financing and other criminal activities may face damaged reputations and significant fines. Compliance failures are prevalent worldwide: Approximately $26 billion worth of fines were levied against banks for AML, KYC and sanctions noncompliance between 2008 and 2018. A report found that the U.S. imposed a full $23.52 billion -- 91 percent -- of those penalties, while European regulators demanded $1.7 billion and the Middle East levied $9.5 million. FinTechs could face these same financial pains as regulators increasingly demand that they follow the compliance rules to which FIs must adhere. The People's Bank of China announced in March that it plans to create rules for regulating and securing the FinTech sector, for example.

Over the past few years, researchers have created a growing number of machine learning (ML)-based face recognition techniques, which could have numerous interesting applications, for instance, enhancing surveillance monitoring, security control, and potentially even forensic art. In addition to face recognition, advancements in ML have also enabled the development of tools to predict or estimate specific qualities (e.g., gender or age) of a person by analyzing images of their faces. In a recent study, researchers at the University of Kwazulu-Natal, in South Africa, developed a machine learning-based model to estimate people's age by analyzing images of their faces taken in random real-life environments. This new architecture was introduced in a paper published by Spinger and presented a few days ago at the International Conference on Computational Collective Intelligence (ICCCI) 2019. Most traditional approaches for age classification only perform well when analyzing face images taken in controlled environments, for instance, in the lab or in photography studios.

In Samuel Beckett's novel, The Unnamable, the anonymous narrator laments, "I'm all these words, all these strangers, this dust of words, with no ground for their setting, no sky for their dispersing." For Beckett's narrator, words have become unmoored from their meaning. They no longer refer to anything in the physical world. Ultimately, they fail to fully convey or contain the inner message that prompted them. It's a deeply unsettling feeling I suspect we've all experienced. Words become disconnected from our emotions, insufficient for what we want to convey.

Governments around the world should create special visa classifications for the international AI and machine learning community, the Partnership on AI said in a newly released report. Such visa classifications should be made for working professionals, as well as students and interns in AI and ML, in order for them to more easily attend conferences and study for extended periods. The report points out that some nations permit special visa classifications for medical professionals, athletes, religious workers, and entrepreneurs. Such steps are necessary, the group asserts, to enable members of the world's AI research community to collaborate and share ideas. Allowing a diverse group of researchers to attend international AI conferences is valuable because it can introduce new ideas, combat groupthink, ensure that a wider swath of researchers enjoy the prestige of major conference presentations, and let "organizations have the opportunity to benefit from all available talent."

Artificial Intelligence (AI), one of the most thrilling and transformative opportunities of our time, is the topic de jour lately with every intersection of intellectual discourse and business discussions sounding in on the potential perks, risks and dangers. Africa's tech ecosystem, one of the most exciting in the world right now, has a growing community of African start-ups that are keen on developing solutions for African problems using this emerging technology. The most desired business outcomes from AAI are: to improve/develop new products/ services; to achieve cost efficiencies, streamline business operations; and accelerate decision making. Enterprises that have enabled AI have reported increased operational efficiency, making faster, more informed decisions and innovating new products and services. To date, strong AI has not yet come into existence, it's still hypothetical hence it exists in the dreams of research scientists and imagination of science fiction writers.

Zygmunt received his PhD degree in Computer Science from the University of Adelaide, Australia in 2013, and his MSc degree in Computer Science from the University of KwaZulu-Natal, South Africa in 2009. He is a senior research fellow at the Australian Institute for Machine Learning. His research lies at the interface of computer vision, machine learning, and challenging industry problems. He develops algorithms that allow computers to perform tasks typically associated with human intelligence. In the last couple of years, his work has focused on the application of machine learning and image processing techniques for the development of smart medical devices.

We plot the judge's probability of the target answer given that sentence against how often humans also select that target answer given that same sentence. Humans tend to find a sentence to be strong evidence for an answer when the judge model finds it to be strong evidence. Strong evidence to a model tends to be strong evidence to humans as shown in Figure 7. Combined with the previous result, we can see that learned agents are more accurate at predicting sentences that humans find to be strong evidence. F Model Evaluation of Evidence on DREAM Figure 8 shows how convincing various judge models find each evidence agent. Our findings on DREAM are similar to those from RACE in §4.2. Figure 8: On DREAM, how often each judge selects an agent's answer when given a single agent-chosen sentence. The black line divides learned agents (right) and search agents (left), with human evidence selection in the leftmost column. All agents find evidence that convinces judge models more often than a no-evidence baseline (33%). Learned agents predicting p ( i) or p ( i) find the most broadly convincing evidence.