Section 8: Various research approaches and methodologies implemented in the research document.

The graph represents a network of 2,229 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 11 August 2019 at 11:32 UTC. The requested start date was Sunday, 11 August 2019 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 12-day, 3-hour, 52-minute period from Monday, 29 July 2019 at 20:00 UTC to Saturday, 10 August 2019 at 23:53 UTC.

Imagine if you had a version of Amazon's Alexa or Google Assistant inside your head, capable of feeding you external information whenever you required it, without you needing to say a single word and without anyone else hearing what it had to say back to you. An advanced version of this idea is the basis for future tech-utopian dreams like Elon Musk's Neuralink, a kind of connected digital layer above the cortex that will let our brains tap into hitherto unimaginable machine intelligence. Arnav Kapur, a postdoctoral student with the MIT Media Lab, has a similar idea. And he's already shown it off. The current AlterEgo device prototype looks a bit like one of those popstar Britney mics, as imagined by the designers of the Star Trek: The Next Generation TV show.

I should have taken heed of the Socratic paradox that'all I know is that I do not know anything', as in January of 2016, I publicly expressed to the scientific and medical community that'There are certain things that a human brain does much better than any piece of technology – such as solving a crossword puzzle or playing the game Go.' In January of 2016, I was in lofty company, as the majority of the big brains of Artificial Intelligence (AI) felt that it would take at least 50 years for a computer to beat any human at Go. Three months later the Google DeepMind Alpha Go system did just that, when it beat not any average human Go player – but the world's 18-time world Go champion, Lee Sedol. This is a non-trivial occurrence. Because there are many tasks that are performed in healthcare each day by humans, that are well suited to be better performed by intelligent thinking machines. For example, the foundation of healthcare – the diagnosis, consists of pattern recognition and algorithms, both of which are superior strengths of machine over humans. My take away from this is that the changes are occurring much more quickly than I realised, not only in the development of AI, but in many other areas such as the global dispersion of high-speed connectivity, blockchain, plummeting costs of data storage, and tremendous improvements in biosensors of all shapes and sizes. The future that many felt was at least 50 years away, appears to already be behind us – and these powerful thinking machines will not stand alone, but will play a central role in our increasing global connectivity.

Artificial intelligence (AI) has become a part of our everyday conversation and lives. It is used when talking about smart and autonomous cars, in digital personal assistants like Siri and Alexa, and in video games. When it comes to incorporating AI into business technology there are obvious applications like business data analytics or customer service products. According to Brian Burke, chief of research at Gartner, AI is a mega-trend that the industry will continue to talk about for the next 20 years. Speaking at the 2018 Gartner Symposium/ITxpo, he said that AI is going to underlie pretty much everything that we do in technology.

There will be dozens of new job titles emerging in the next few years that In a world where inequality seems to be on an inexorable rise, will the proliferation of AI be the solution that closes the gap between the privileged and underprivileged? If it's used ethically, then all the potential is there. Even though quality of life around the world has improved by almost all metrics over the last 50 years, from literacy to life expectancy, the divide between the'haves' and'have-nots' tends to dominate the news headlines, and it would be wrong to pretend that there are not endemic issues which need to be addressed if as a society we want to strive for true equality. It's most likely a fool's errand to try and'fix' the system from the top down. But to try and make sure that everyone gets access to the quality of life most of us are already lucky enough to enjoy, there's plenty that can be done from the bottom up. Primary amongst these is the potential of artificial intelligence to continue improving the quality of life for the minority who still suffer through living in poverty, without access to proper healthcare, or those who struggle to access the services society can and should provide.

DeepMind, the U.K.-based AI research subsidiary acquired by Alphabet in 2014 for $500 million, today detailed ecological research its science team is conducting to develop AI systems that'll help study the behavior of animal species in Tanzania's Serengeti National Park. It hopes to expedite the analysis of data from hundreds of motion-detecting field cameras, which have captured millions of images since they were deployed by the Serengeti Lion Research program over nine years ago. "The Serengeti is one of the last remaining sites in the world that hosts an intact community of large mammals … As human encroachment around the park becomes more intense, these species are forced to alter their behaviours in order to survive," wrote DeepMind in a blog post. "Increasing agriculture, poaching, and climate abnormalities contribute to changes in animal behaviors and population dynamics, but these changes have occurred at spatial and temporal scales which are difficult to monitor using traditional research methods." For nearly a decade, conservationists have tapped the aforementioned cameras to keep tabs on animals within the park's core, enabling them to study their distribution and demography.

What is it exactly that makes humans so smart? In his seminal 1950 paper, "Computer Machinery and Intelligence," Alan Turing argued human intelligence was the result of complex symbolic reasoning. Philosopher Marvin Minsky, cofounder of the artificial intelligence lab at the Massachusetts Institute of Technology, also maintained that reasoning--the ability to think in a multiplicity of ways that are hierarchical--was what made humans human. Patrick Henry Winston begged to differ. "I think Turing and Minsky were wrong," he told me in 2017. "We forgive them because they were smart and mathematicians, but like most mathematicians, they thought reasoning is the key, not the byproduct." Winston, a professor of computer science at MIT, and a former director of its AI lab, was convinced the key to human intelligence was storytelling. "My belief is the distinguishing characteristic of humanity is this keystone ability to have descriptions with which we construct stories. I think stories are what make us different from chimpanzees and Neanderthals. And if story-understanding is really where it's at, we can't understand our intelligence until we understand that aspect of it."

Zindi is convening Africa's data scientists to create AI solutions for complex problems. Founded in 2018, the Cape Town-based startup allows companies, NGOs or government institutions to host online competitions around data-oriented challenges. Zindi's platform also coordinates a group of more than 4,000 data scientists based in Africa who can enroll to join a competition, submit their solution sets, move up a leader board and win the challenge -- for a cash prize payout. The highest purse so far has been $12,000, split across the top three data scientists in a competition, according to Zindi co-founder Celina Lee. Competition hosts receive the results, which they can use to create new products or integrate into their existing systems and platforms. Zindi's model has gained the attention of some big corporate names in and outside of Africa.

Path-planning algorithms are an important part of a wide variety of robotic applications, such as mobile robot navigation and robot arm manipulation. However, in large search spaces in which local traps may exist, it remains challenging to reliably find a path while satisfying real-time constraints. Efforts to speed up the path search have led to the development of many practical path-planning algorithms. These algorithms often define a search heuristic to guide the search towards the goal. The heuristics should be carefully designed for each specific problem to ensure reliability in the various situations encountered in the problem. However, it is often difficult for humans to craft such robust heuristics, and the search performance often degrades under conditions that violate the heuristic assumption. Rather than manually designing the heuristics, in this work, we propose a learning approach to acquire these search heuristics. Our method represents the environment containing the obstacles as an image, and this image is fed into fully convolutional neural networks to produce a search heuristic image where every pixel represents a heuristic value (cost-to-go value to a goal) in the form of a vertex of a search graph. Training the heuristic is performed using previously collected planning results. Our preliminary experiments (2D grid world navigation experiments) demonstrate significant reduction in the search costs relative to a hand-designed heuristic.