AI had a good run in 2020. Amazing breakthroughs of 2020 made looking forward to 2021 a delight. 2020 showed there are no bounds to technology, particularly AI. The year might be young but development in AI has shown no sign of taking a break. It continues to spawn new developments that will make AI more independent making life easier and better for humans.
Here are Some of the most popular developments so far in 2021
Customized brains for robots
For humans, reacting to our environment is easy. When it suddenly starts raining we know to take cover, we scan the area and look for a shelter close by and hide. Unless we love rain and don’t mind getting wet. Also, interacting with other people is seamless and fluid, we know when a person stretches out their hand as a call for help or a handshake.
For robots, it’s not as easy reacting especially with interacting with people. They might have fast and powerful mechanical motors but their reaction time and response to stimuli is slow
To make robots keep up with humans during interactions, researchers have developed an automated way to design hardware that would increase the speed of a robot’s operation. The system is called robomorphic computing. It assesses the robots physical layout, tasks intended for the robot and suggests an optimized hardware chip architecture that increases the robots speed in responding to stimuli.
Robots calculate everything. They perceive stimuli and try to prepare a response. This takes a lot of computing power and a long time.
The research will be presented by Sabrina Neuman, a recent PhD graduate of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). According to her, it would be great if there could be a robot that would reduce risk for healthcare workers and patients.
Human brain as AI software, helps computers learn.
Researchers have discovered that the human brain doesn’t need as much information as a computer brain to learn. Computer brains are therefore going to be given more human brain features to learn faster and with little information.
According to Science Daily, Professor of Neuroscience, Maximilian Riesenhube and Dr Joshua Rule have discovered that computer-based intelligence can work like human intelligence when they’re programmed to use faster techniques to learn new objects. According to them, the model would make it possible for artificial neurons to learn new things in a way that could be considered biological. This biologically plausible technique for artificial intelligence to learn will require little examples of visual concepts.
Humans can learn something new by just seeing it. A few months old baby can easily tell the difference between a lion and a tiger with little examples. Maximilian and Joshua are trying to help AI learn this way too. The old learning techniques required a lot of information about a concept for AI to learn that concept.
Super intelligent machines might be beyond the control says, scientists
AI is no doubt a fascinating technology. News of the continuous progress being made in the sphere is truly intriguing but a super-smart artificial machine beyond our control poses danger.
The dangers of AI becoming self-aware and taking over the world is not a new topic. While sci-fi movies do a really good job of telling us what it could look like, researchers are putting facts and figures together to tell us that – super-intelligent machines will be beyond our control.
The study shows that an algorithm that would stop a super-intelligent AI from destroying the world would not work. So according to the calculations, we would not be able to stop the AI from doing that could harm us.
The developments that have been made in AI with just some days into 2021 are laudable. The year already promises to be an eventful one for AI. We might just see more technological advancements indistinguishable from magic.
How AI Is Helping Fintechs Provide Intelligent And Better Financial 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.
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.
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.
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.
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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