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Artificial Intelligence

4 most interesting Artificial Intelligence breakthroughs of 2020



AI breakthroughs of 2020

2020 is one year most people won’t forget so easily whether for good or for bad. One unique feature of the year, however, is that it spawned unique problems and provoked unique solutions. 

The potency of technology was really put to the test in 2020. It proved that humans are more reliant on technology that we thought we were. The digitization of most aspects of our lives exposed easier and effective ways of running things . 

The pandemic is undoubtedly the highlight of 2020. A lot of tech corporations were afforded the chance to flex some serious tech muscles. Technology kept a lot of things running while the world was on lockdown. Our reliance on the internet was unprecedented. Not only did it provide a means of running daily activities, it was a way of maintaining social connections.

The jaw dropping numbers raked in by some tech companies shows that technology rescued us in many ways. From keeping us sane by keeping us connected and helping us earn by keeping businesses afloat, to directly helping in the fight against COVID-19. 

Artificial intelligence is one tech that has not only positioned itself as a piece of technology, but also as a part of our lives. Applications of AI, prior to 2020, were vast. It had been helping before the pandemic but it became more important and saw more breakthroughs in 2020. AI had astounding breakthroughs in healthcare, business and education. It equally had its lapses. Nonetheless, it would undoubtedly form an integral part of human existence here on out. 

Here are some of the most interesting AI stories of 2020.

Using AI for COVID-19 side effects

Some time in November, the MHRA (Medicines and Healthcare Regulatory Authority), paid U.K software company, Genpact, £1.5 million to create an AI tool that could sift through a lot of the reports that entailed adverse reaction to COVID-19 vaccines. According to the reports, the AI will be able to sift through massive amounts of ADR (adverse drug reaction) reports to help make sure no detail is missed. 

Why this tool matters.

The U.K is preparing for a widespread vaccination. The vaccines are relatively new and will be administered to a lot of people with unique immune systems. The large amount of data that will  need processing will be monumental. Not only will it take time if it were done by humans, but there would also be errors. 

The administering of the vaccines needs to be accelerated. In the process, important details need to be properly analyzed without errors. The creation and administering of the vaccines in record time would not have been possible without AI machines such as this. Though a few number of people have gotten vaccinated, AI played a crucial role leading up to this point.

AI solves 50-year-old science problem

Protein folding is one problem that has been running circles round scientists for many years. The specifics of this problem are quite complex but according to scientists, solving it could play a major role in developing cures for diseases such as cancer. The AI program known as Alpha Fold, created by Google, claims to be able to solve this problem. Alpha Fold will be able to unfold over 200 million proteins present in some diseases, thereby, essentially helping scientists to understand how they work and invariably developing a cure. We can only hope to hear more positive news about Alpha Wolf and how it can help cure serious diseases. 

Robots taking over construction sites. 

The “men at work” sign which is usually seen at construction sites might someday read “robots at work”. AI didn’t stop at healthcare this year, it took its expertise to construction sites too. This time, AI isn’t doing brain work like calculating or sifting through loads of information, it is getting very physical. In an article by WIRED, a company called Canvas has built AI powered robots that could work at construction sites and work just as well as humans. Tasks such as drywalling, which requires a lot of skills due to its artistic nature, can be easily done by the robot. The robot has already been used at different construction sites but it is still very far from mainstream adoption. Seeing robots working alongside humans at construction sites could be a trend in coming years. 

Your vacuum cleaner could be listening to you.

While AI displayed laudable exploits on how it could be of help to humans, it also revealed a bit of its dangerous side. Our privacy as regards the use of AI is a subject that has been debated in no small amount. Household electronics are now built to be able to communicate with each other. A vacuum cleaner now has a microphone that could serve as a listening device for hackers. 

A research carried out by a team from the National University of Singapore has demonstrated that devices that use Lidar technology, can be used to send sound signals without even having a microphone. The Lidar technology helps the vacuum detect objects and move round them. 

Now that most of our work is done online  and from home, this could be a very serious security challenge. Business information shared online might be picked up and compromise a business operation within seconds. 

A lot of breakthroughs are currently happening in the world of AI. 2020 recorded some impressive AI applications. Whilst some of these breakthroughs are still in their early stages, wide adoption in subsequent years is very plausible. 


Bolu Abiodun is a recent graduate of Theatre and Media Arts, Federal University Oye-Ekiti. A journalist with over a year's experience on the job. A former editor at American Media company Project Forward, he is a skilled content creator, social media manager and digital marketer.

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Artificial Intelligence

How AI Is Helping Fintechs Provide Intelligent And Better Financial Services



AI fintech services

We live in an era of data. In today’s world, data is the new gold. The quality of services now significantly depends on how much insight can be extracted from data to help in the creation of the services. For fintech organizations, building services that harness the power of data and artificial intelligence has now become necessary to ensure that the services are tailored to meet the needs of customers. Artificial intelligence is now being used in various ways to help fintech companies provide intelligent and improved services. Some of the major areas of AI application in fintech are discussed in this article.

Risk Assessment

From insurance companies to banks and other fintech institutions, assessing credit worthiness and estimating the level of risk associated with every transaction has become very crucial. Now, many fintech companies employ the use of AI in determining the credit profiles of clients which helps to minimize financial losses when customers fail to repay loans or meet other financial commitments. 

Predicting and preventing fraudulent transactions is another challenge that fintechs are using AI to solve. Using machine learning algorithms, fintech organizations are able to build more accurate fraud detection mechanisms to curb the activities of scammers. The advantage of using machine learning for fraud detection in financial systems is that the machine learning model can learn from the financial data by itself. Thus, it is able to uncover hidden patterns and make a more robust prediction compared to traditional fraud detection algorithms. AI-based fraud detection algorithms can also be used to verify insurance claims and flag fraudulent ones. 

Churn Prediction

Customer churn is an important Key Performance Index (KPI) for any organisation. Preventing customer churn is aimpoaaaustomers and improve customer engagement. Many fintechs across the world now use AI to increase customer retention by understanding customer behaviour and making data-driven decisions to retain the audience of customers.

Intelligent Customer Service

Customer service is an aspect of fintech that has been significantly transformed by AI. The use of AI in this area has drastically reduced the need for human customer care representatives and the cost associated with employing these representatives. With AI, more customers can be attended to more efficiently via chatbots, virtual assistants etc. 

Chatbots are, particularly, one of the most common uses of AI in fintech customer service. Chatbots are sophisticated conversational AI applications that can engage with customers, address complaints and basically fill in the gap of a human employee. Chatbots have now become faster and easier means for customers to fix issues they have while using fintech services.

The Future of Fintech With AI

The use of AI in financial technology extends beyond risk assessment, churn prediction and intelligent customer service. Areas like payment processing and sentiment analysis are also being transformed by AI. Organizations like MasterCard and Visa have been able to improve the quality of their services by leveraging AI to achieve this. Personalized banking and financial services will define the future of financial technology. Better experiences will be developed for each customer in a unique and personalized manner. This may be impossible without AI. The future of fintech is geared towards smarter and more intelligent services, with AI steering the wheel to this future.

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Artificial Intelligence

This New AI Can Tell The Kind Of Faces You Find Attractive



AI researchers are constantly coming up with ways to make the technology serve us better. However, while these services are impressive they can be a tad creepy. A time when social media will be able to like pictures on our behalf, based on how our brain reacts to the picture sounds quite uncomfortable. 

Although Instagram isn’t working on an algorithm that tracks brain waves, researchers from the University of Helsinki and Copenhagen University are. This AI matchmaker can tell when you find someone attractive. It creates a database of this information and knows the kind of faces you find attractive or in more lucid terms, “your type”

Here’s how it works

According to Digital Trends, the researchers used a generative adversarial neural network. This network has over 200,000 pictures of celebrities. The AI then recreates original faces based on that database. A group of participants kitted with electroencephalography (EEG) caps were shown these images. Concentrating on which picture they found attractive, the cap read their brain waves to know how they felt about the picture. 

Michiel Spapé, one of the researchers, said, “By capturing the brain waves that occurred just after seeing a face, we estimated whether a face was seen as attractive or not. This information was then used to drive a search within the neural network model — a 512-dimensional ‘face-space’ — and triangulate a point that would match an individual participant’s point of attractivity.”

The AI uses machine learning to connect the dots as per what we are attracted to. Whatever pictures cause a spike in brain waves is stored, the machine analyzes them and looks for the faintest detail they have in common. Afterwards, it comes up with facial features we might not know we are attracted to. 

So, how do you measure attraction?

The researchers have found out that roughly 300 milliseconds after a participant sees an attractive image, the brain lights up. Well, a living brain lights up every time but this particular “lighting up” gives off a specific signal identified as a P300 wave. A P300 wave in itself, does not mean you’re attracted to something. It simply means you have spotted what you have been asked to look out for. The participants have been asked to look out for images they find attractive, as such, any discovery of a P300 wave at that time, means that they find the picture attractive. 

Great algorithm for dating apps?

An algorithm that can tell which facial features you find attractive will be great for dating apps. It will recommend to users, the kind of faces they will probably find attractive. 

However, one can argue that the laws of attraction aren’t that simple. Facial features are just a facet of what people find attractive. Nevertheless, there could be an integration of the detection of all the possible attraction facets. This would go a long way in cementing AI’s position as a major partaker in human lives- or love lives, as the case may be.

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Artificial Intelligence

Using AI To Grow Avocados And Improve Agricultural Productivity In South Africa



Globally, South Africa maintains a spot as one of the major producers of avocado. In 2019, the nation secured approximately 1.1% international market share in the multi-billion dollar avocado export market. However, its annual production volume of between 80,000 and 120,000 tonnes is still many miles behind the annual production volume of 1.5 million tonnes by top avocado producer, Mexico. However, this figure could be significantly increased with precision agriculture, thereby, providing ample opportunity to boost the nation’s overall revenue from avocado export.

The right amount of irrigation is extremely critical for South African avocados to attain maximum growth. For a long time now, South African farmers have relied on traditional irrigation strategies that are largely hinged on their intuition and experience, which has hindered the attainment of the maximum productivity that could be reached in avocado production. Cracking the issue of irrigation could unlock an astonishing increase in South Africa’s annual avocado production volume.

Now, Israeli company, SupPlant, is using technology, particularly AI, to provide farmers with smarter and more efficient ways to improve agricultural productivity. They have been able to achieve this by developing an AI model that employs predictive algorithms to provide customized irrigation recommendations based on analyzing about 100 million data points. Their solution is now being used by South African farmers to grow avocados as the solution is particularly useful for South Africa’s avocado irrigation challenge.

To gather data, SupPlant’s solution involves the use of sensors. These sensors are located in 5 different parts of the plant (avocado, deep soil, shallow soil, leaf, stem/trunk). The sensors monitor plant stress, the exact water content in the soil, health data of the plant, the growth patterns of the plant and the fruit. Climatic data and data on the growth patterns of the plant are also monitored by the sensors. The combination of the data is uploaded to a cloud-based algorithm, at 30-minute intervals, which then makes predictions based on the input data from the sensors to provide farmers with irrigation recommendations.

However, the solution above does not solve all of the challenges that South African farmers face in avocado production. Another critical challenge for the farmers is associated with the weather. To overcome this hurdle for South African farmers, SupPlant integrates the use of top weather intelligence platform, ClimaCell, to monitor the weather in a particular plot of land. Today, with the use of SupPlant’s mobile app, South African farmers can monitor their farm plots and control irrigation on any plot from anywhere. The mobile app provides graphical information on historical and future irrigation plans, present and forecasted climatic data customized for each farm plot, growth patterns of the avocado plant, agronomic insights and recommendations for irrigation.

If, for instance, plant stress begins to increase, farmers will immediately get altered via the mobile app and receive data-driven amplified irrigation recommendations to prevent severe damages to the plant. With SupPlant’s solution in the hands of farmers, farmers will no longer need to engage intuitive and traditional irrigation and farming approaches.

In such a time as now, when the demand to produce more in smarter and more efficient ways is seemingly high, SupPlant’s solution evidently lies beyond South Africa’s avocado industry, as it also extends to other areas where precision agriculture could be harnessed to unlock massive agricultural productivity. With a technology like AI, the journey to smarter and more intelligent farming is just evolving.

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