Mediaboss Marketing

BY BASIRAT ISHOLA

Artificial intelligence, which is also abbreviated as AI, refers to the branch of computer technology that focuses on developing machines that have the capacity and intelligence to replicate or copy human functions as closely as possible. They are usually employed for such functions as speech recognition, planning of tasks, learning and solving diverse problems in different professions.

As it is the case with modern advancements, there are appreciable advantages associated with the use of AI, coupled with some drawbacks, both existing and forecasted by experts and enthusiasts. Some of these are explained below.

Pros

A key advantage has to do with its ability to deliver quite precise results. In other words, the usual human errors that can come up as a result of fatigue or distractions can be totally eliminated by employing the use of a well-designed and programed AI machine. The end result is improved accuracy and greater delivery.

Fraud detection in smart card-based systems is possible with the use of AI. AI machines are quite efficient when deployed to sniff out fraud in smart card based systems. This has been extensively deployed in industries that employ the use of smart cards, such as cable TV organisations, banks etc.

In addition to the above, it has become more common to deploy intelligent machines to carry out dangerous assignments such as detonation of explosive devices, such as land mines and bombs. This is usually due to their ability to work very fast and effectively, without stress or tension that can result in errors.

It has also become quite common in the medical field. The use of algorithms has simplified the area of diagnosis e.g. neurological disorders; training of medical personnel via simulators like surgeons on delicate procedures. They are often used to carry out repetitive functions that are time consuming in diverse professional fields.

From the above, it is very obvious that the strength of artificial intelligence that invariably lures a lot of fields into deploying them is drawn from their ability to function efficiently under minimum supervision, without the need to take a nap and are never bored or frustrated as long as they are properly programmed and maintained. They also help to minimise or eliminate health and safety risks to humans, when used as substitutes to carry out hazardous jobs.

Cons

There are a number of drawbacks associated with the deployment of AI, paramount amongst them is the huge cost implication it attracts in the area of maintenance, replacement and repair of parts and upgrade. There is also the challenge of restoration of lost codes, which can be quite rigorous, time consuming and expensive.

Another concern, which some people may term sentimental but which is in any case important, is that of ethics and morals. Are we not usurping the position of God, the creator, by imputing human intelligence in machines? Shouldnt human intelligence be left to natural forces?

It is unarguable that AI machines have the capacity to carryout repetitive jobs for extended periods, but they lack the capacity to learn from this process of repetition and get better on the job like humans. They cannot deviate from what they are programed for. This has its advantages, but in situations where there is a need to adjust to an unforeseen development, it becomes a major handicap. In such situations the input of man becomes quite indispensable.

In this present time of heightened global challenges with terror, it becomes quite risky to allow AI that can be used to carryout dangerous tasks fall into the wrong hands. This can cause devastating consequences especially in the area of nuclear warfare and the use of robots to convey bombs into crowded environment.

Ishola, a lead data scientist and business intelligence specialist, writes from Lagos.

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Pros and cons of artificial intelligence - TheCable

Job Description

The National University of Singapore invites applications for the position of Research Fellow in the Department of Computer Science, School of Computing (SoC). SoC is strongly committed to research excellence in all its dimensions: Searching for fundamental results and insights, developing novel computational solutions to a wide range of applications, building large scale experimental systems, and improving the well-being of society. We seek to play an active role both internationally and locally in the core and emerging areas of Computer Science and Information Systems.

The Research Fellow will be responsible for working closely with the Principal Investigator on one or more research projects. The research fellow will help the Principal Investigator conduct research in Artificial Intelligence (AI) by offering new theoretical perspectives on and engineering approaches to human aware decision making. In particular, the research fellow is going to explore new conceptual and computing frameworks for decision making in complex, uncertain, and changing environments. Special focus will be placed on the underlying mechanisms and dynamics of integrating learning with planning and decision making, representation change, perception, communication, and interaction in real-world environments.

The candidate should possess experience or interest in at least one of the following research areas:

Decision-theoretic AI, relational and probabilistic modelling, causal modelling, statistical learning with multimodal information, planning and model-based reinforcement learning, transfer learning in sequential decision processes, knowledge- and data-driven representation, computational neuroscience, computational cognitive science, and AI applications in health and education.

The position is available immediately. The initial term of appointment will be 1 year with possible extension to 2 years. The selected candidate will be offered strongly competitive salaries and benefits, support to travel for dissemination of research results, and substantial freedom to build a strong research profile of his/her own.

Qualifications

A PhD degree in relevant areas strong system-building and management skills; keen interests in working with emerging technologies and frameworks; good communications skill in both written and verbal English.

More Information

Location: Kent Ridge CampusOrganization: School of ComputingDepartment : Department of Computer ScienceEmployee Referral Eligible: NoJob requisition ID : 11665

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Research Fellow, Artificial Intelligence and Human Aware Decision Making job with NATIONAL UNIVERSITY OF SINGAPORE | 278306 - Times Higher Education...

From using computers to work faster to now teaching computers to work themselves, weve achieved quantum leaps when it comes to the possibilities of computer technology. Driving this new generation forward are the developments weve made with Artificial Intelligence and Machine Learning.

Algorithms dictate processes, solve problems, perform calculations, and more. What Artificial Intelligence does is cut out the middle-man us for the most part with the help of Machine Learning. To simplify, Machine Learning creates algorithms that AI systems can use to process data and learn new things without having to be programmed. Detailed algorithms are now capable of learning on the fly and adapting to situations using big data and deep learning at almost instantaneous speeds. For instance, AI systems with Machine Learning capabilities can recognize faces, detect instances of fraud, predict customer behaviour, and much more.

The past decade has seen the most proliferation of this technology, with everything from social media platforms, OTT content providers to even matrimonial services employing some form of this technology. So, its only natural that online gaming, a multi-billion-dollar industry, had its interest piqued by its possibilities and the possibilities are vast!

How are Machine Learning and Artificial Intelligence improving gaming (and vice versa)?

Gaming and Artificial Intelligence is a match made in technology heaven. AI&ML helps with improving the in-game and product experience for the players by powering a more personalized experience. It also has applications in content marketing by helping create user journeys with increased efficiency, leading to a landscape where users have instant and reliable access to what they want, cutting through the clutter.

Advanced analytics can help the player analyse their gameplay, reflect on it, and develop new strategies. Real-time probability analysis helps the player calculate the odds of a win. The introduction of metrics like VPIP in online poker, which conveys the frequency of a player participating in a hand, has given gamers a quantitative and qualitative overview of their own as well as their opponents performances. Online gaming thus becomes more than just a click of a button and starts to feel just as real, if not more.

This is even more so true when you look at the back-end benefits of employing AI&ML. Fraud is heavily mitigated, thanks to technology capable of detecting anomalies in real-time. Customer experience is vastly improved as AI helps online games reach the right gamers far more easily. Player protection becomes much more rapid, allowing online platforms to help users play responsibly. Harnessing this technology can truly make online gaming a far safer experience for the player.

Another benefit is the cost-efficiency of the technology. As the technology becomes more prevalent, we see an improvement in several aspects of a gamers online journey. Things like online payments, security, and customer support become far more efficient and will steadily undergo transformation and improvements over the next few years. Chatbots are one such example where the user experience can be elevated by providing faster and efficient support to customers.

On the flip side, this surge in adopting this technology now witnesses rapid developments for the technology as well. In an industry where decisions are made in milliseconds, we need technology that can work in nanoseconds. So, we now see huge resources being dedicated to enabling just that. Industry giants sense the underlying possibilities and have specialized departments to focus on the improvement of these Artificial Intelligence algorithms. It wouldnt be surprising to see generational leaps in the capabilities of AI as a whole. While they say its unnecessary to reinvent the wheel, as gamers, were inclined to try and make the wheel go faster!

Views expressed above are the author's own.

END OF ARTICLE

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Intelligent gaming: How Artificial Intelligence and machine learning raise the stakes - The Times of India Blog

Students with basic knowledge of machine learning can apply for an online course on applied artificial intelligence (AI) offered by select Indian Institutes of Technology (IITs).

The course will teach ways to implement AI in industrial use and domains like healthcare, applications in smart city projects and so on.

The course, which includes demonstrations and code walkthroughs and industrial use-cases, is part of the ongoing National Supercomputing Mission (NSM). This six-year-old mission is jointly being led by the Centre for Development of Advanced Computing (CDAC) and Indian Institute of Science under the aegis of the department of science and technology and the electronics and IT ministry.

The online course, to be jointly conducted by IITs Kharagpur, Madras, Palakkad and Goa will cover topics like fundamentals of AI accelerators and system setup, accelerated deep learning, end-to-end accelerated deep science and industrial use-cases of accelerated AI.

For registrations and further details, applicants can visit iitgoa.ac.in/aishikshaai/schedule.php

The 33-session long course will commence on January 31 and is best suited for students in their third and fourth years of engineering from any stream, science postgraduates, PhD scholars and working professionals.

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CDAC, IITs to jointly offer online course on artificial intelligence - The Indian Express

Rhinolophus rouxi, which inhabits parts of South Asia, was identified as a likely but undetected betacoronavirus host by the study authors. Credit: Brock and Sherri Fenton

Daniel Becker, an assistant professor of biology in the University of Oklahomas Dodge Family College of Arts and Sciences, has been leading a proactive modeling study over the last year and a half to identify bat species that are likely to carry betacoronaviruses, including but not limited to SARS-like viruses.

The study Optimizing predictive models to prioritize viral discovery in zoonotic reservoirs, which was published by Lancet Microbe, was guided by Becker; Greg Albery, a postdoctoral fellow at Georgetown Universitys Bansal Lab; and Colin J. Carlson, an assistant research professor at Georgetowns Center for Global Health Science and Security.

It also included collaborators from the University of Idaho, Louisiana State University, University of California Berkeley, Colorado State University, Pacific Lutheran University, Icahn School of Medicine at Mount Sinai, University of Glasgow, Universit de Montral, University of Toronto, Ghent University, University College Dublin, Cary Institute of Ecosystem Studies, and the American Museum of Natural History.

Becker and colleagues study is part of the broader efforts of an international research team called the Verena Consortium (viralemergence.org), which works to predict which viruses could infect humans, which animals host them, and where they could emerge. Albery and Carlson were co-founders of the consortium in 2020, with Becker as a founding member.

Despite global investments in disease surveillance, it remains difficult to identify and monitor wildlife reservoirs of viruses that could someday infect humans. Statistical models are increasingly being used to prioritize which wildlife species to sample in the field, but the predictions being generated from any one model can be highly uncertain. Scientists also rarely track the success or failure of their predictions after they make them, making it hard to learn and make better models in the future. Together, these limitations mean that there is high uncertainty in which models may be best suited to the task.

In this study, researchers used bat hosts of betacoronaviruses, a large group of viruses that includes those responsible for SARS and COVID-19, as a case study for how to dynamically use data to compare and validate these predictive models of likely reservoir hosts. The study is the first to prove that machine learning models can optimize wildlife sampling for undiscovered viruses and illustrates how these models are best implemented through a dynamic process of prediction, data collection, validation and updating.

In the first quarter of 2020, researchers trained eight different statistical models that predicted which kinds of animals could host betacoronaviruses. Over more than a year, the team then tracked discovery of 40 new bat hosts of betacoronaviruses to validate initial predictions and dynamically update their models. The researchers found that models harnessing data on bat ecology and evolution performed extremely well at predicting new hosts of betacoronaviruses. In contrast, cutting-edge models from network science that used high-level mathematics but less biological data performed roughly as well or worse than expected at random.

Importantly, their revised models predicted over 400 bat species globally that could be undetected hosts of betacoronaviruses, including not only in southeast Asia but also in sub-Saharan Africa and the Western Hemisphere. Although 21 species of horseshoe bats (in the Rhinolophusgenus) are known to be hosts of SARS-like viruses, researchers found at least two-fourths of plausible betacoronavirus reservoirs in this bat genus might still be undetected.

One of the most important things our study gives us is a data-driven shortlist of which bat species should be studied further, said Becker, who adds that his team is now working with field biologists and museums to put their predictions to use. After identifying these likely hosts, the next step is then to invest in monitoring to understand where and when betacoronaviruses are likely to spill over.

Becker added that although the origins of SARS-CoV-2 remain uncertain, the spillover of other viruses from bats has been triggered by forms of habitat disturbance, such as agriculture or urbanization.

Bats conservation is therefore an important part of public health, and our study shows that learning more about the ecology of these animals can help us better predict future spillover events, he said.

For more on this research, see Shall We Play a Game? Researchers Use AI To Search for the Next COVID/SARS-Like Virus.

Reference: Optimising predictive models to prioritise viral discovery in zoonotic reservoirs by Daniel J Becker, PhD; Gregory F Albery, PhD; Anna R Sjodin, PhD; Timothe Poisot, PhD; Laura M Bergner, PhD; Binqi Chen; Lily E Cohen, MPhil; Tad A Dallas, PhD; Evan A Eskew, PhD; Anna C Fagre, DVM; Maxwell J Farrell, PhD; Sarah Guth, BA; Barbara A Han, PhD; Nancy B Simmons, PhD; Michiel Stock, PhD; Emma C Teeling, PhD and Colin J Carlson, PhD, 10 January 2022, The Lancet Microbe.DOI: 10.1016/S2666-5247(21)00245-7

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Artificial Intelligence Used To Search for the Next SARS-COV-2 - SciTechDaily