Artificial Intelligence is no more just a concept from sci-fi books and movies. As a matter of fact, now AI is present in our everyday lives, even if the contemporary world still doesn’t look like a sci-fi movie. However, AI as we know it still hasn’t reached the level of superintelligent machines that would surpass human intellectual competencies. The question is, will AI be able to reach this stage of development in the coming years? What is the probable future of AI and what are the most significant trends in machine learning that we will witness coming into effect in the near future? Read our article to find out! 

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How intelligent is current AI?

Artificial Intelligence is a term coined in 1955, but especially the last decades have been marked by rapid development of this area of computer science. When we talk about the current applications of Artificial Intelligence, we mean one type of AI that is available right now: it’s called Artificial Narrow Intelligence (ANI). ANI, also known as applied AI, is able to perform domain-specific reasoning tasks and problem solving, such as:

  • speech and text recognition: thanks to Natural Language Processing (NLP), a subfield of AI, program computers can process and analyze human (natural) languages, making possible the interactions between computers and humans in natural language. One of the most common applications of NLP is in voice assistants: Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana, etc.
  • computer vision: the ability of a computer to process, analyze and understand  digital images through the transformation of the visual input. The applications of computer vision are various: object or facial recognition, image restoration, motion analysis, etc. 
  • autonomous vehicles, like these being developed by Tesla. Self-driving cars, although yet not so common on our roads, are an excellent example of the impact of AI on everyday life. Multiple AI subfields must be put together in order to make a vehicle that will replace human-driven cars: deep learning, speech recognition, voice search, image recognition and processing, motion detection, etc. 
  • product recommendation systems, widely used by ecommerce shops. AI-powered recommendation engines analyze consumer behavior and preferences to deliver accurate, personalized product recommendations. Ecommerce giants have successfully implemented AI recommendations in their marketing strategies: for instance, 35% percent of Amazon’s revenue is generated by its recommendations engine, and Zalando uses content-based and collaborative filtering algorithms to provide users with tailor-made product recommendations. 

These problems require processing huge amount of data, something impossible for a human brain, but easily done for a well-trained AI algorithm. Nevertheless, it doesn’t mean that a computer program performing these tasks is fully intelligent, as an algorithm built and trained for one purpose won’t be able to perform well in a completely different task. This limitation makes it hard to recognize current AI applications as a true AI. Actually, in 2017, intelligence tests conducted with some popular and publicly available ANI such as Google AI and Siri revealed that these AI achieved a score of about 47 IQ points, which is a level of a six-year-old child. 

However, the ANI is universally acknowledged to be only one, least developed of the possible types of AI. What are the other types and how intelligent can they become?

AGI: the future of Artificial Intelligence

The increasing computing power of modern computers and vast amount of data available to feed the algorithms allow assuming that we’re on the way to reach the next levels of AI and machine learning development.

The next-level AI that is believed to appear in the future is known as Artificial General Intelligence (AGI) Right now, there are no AGI solutions available on the market, which means that at the moment ANI is the only type of AI that the humanity is able to build, therefore any other AI types are purely theoretical. On the other hand, lots of resources are spent every year on research, and there are many theories on how the advanced types of AI can be built, and although with no success yet, it gives hope that someday it will be achieved.

In short, Artificial General Intelligence (also known as Deep AI or Strong AI) is believed to be able to mimic human intelligence and behavior so well that it will become indistinguishable from the human mind in terms of learning and understanding any task, therefore successfully surpassing Turing test. AGI wouldn’t be limited to performing specific tasks, like ANI is, and could be used for multiple purposes. The concept of human-like intelligent machines can feel disturbing, and indeed, it has caused concerns: in 2015, an open letter on artificial intelligence was signed by a number of scientists and researchers, among them Stephen Hawking and Elon Musk. The letter emphasized the importance of using the increasingly advanced AI systems for the societal benefit, and researching “how to reap its benefits while avoiding potential pitfalls”, in other words: avoiding potential dangers for the humanity. 

However, even that there is a lot of effort and resources put into AGI research, it’s not expected to be built in the next couple of years. In the most optimistic scenario, there are decades more research required to develop a true AI.

XAI: Explainable Artificial Intelligence

Another future AI trend that is being talked about is the XAI – Explainable Artificial Intelligence. The term hints that this type of AI would be able to explain itself. Nowadays, there are many industries and use cases where machine learning methods give results that aren’t easy to interpret. For instance, data scientist sometimes can struggle to understand what has led AI to make a specific decision, let alone explain it to another person, for example the customer. The concept of XAI, an AI that would explain its own decision-making process, was born from the need of being able to interpret the algorithms’ way of reasoning, as it also could allow to improve it and avoid its mistakes.  

The main obstacle in the development of XAI is that neural networks mostly are not so easy to explain, as the layers return weights matrices. There are already some ideas being implemented, like heat maps for the weight matrices that improve neural networks interpretability. For sure more research will be done in this area in the upcoming years.

The evolution of deep learning

Deep learning surely will be still a hot topic in the next decades and one of the most significant machine learning trends, as there is still a lot of work that can be done in this field. We see new methods introduced each year, that perform better than other methods or allow to use deep learning in brand new areas. You can stay up to date with the recent developments and breakthrough machine learning trends and methods thanks to events such as the annual NeurIPS conference. 

What are some of the most interesting trends in deep learning?

  • Capsule neural networks, or CapsNet: it’s a type of neural network that can improve hierarchical relationships models to better mimic human neurons.
  • FractalNet: it’s an attempt to build ultra-deep neural network as an alternative to deep residual learning. 
  • Deep reinforcement learning: this ML trend mixes deep learning (ie. deep neural networks) with reinforcement learning (Q-learning, actor critic, etc.) in order to create powerful algorithms that might be able to solve complex problems.

 

We leverage Machine Learning trends to help you stay one step ahead of the competition

At Codete, we have a vast experience in developing state-of-the-art AI, machine learning and deep learning solutions that support businesses and humans in achieving goals. Our R&D teams excel at driving digital transformation by developing solutions based on Machine Learning, AI & Deep Learning for global clients in numerous fields, such as FinTech, automotive, energy, HealthTech, etc. Among our multiple ML projects, it’s worth mentioning the following:

  • Natural Language Processing (NLP) and sentiment analysis: as we have explained before, NLP algorithms analyze human language. Codete has used NLP to develop a sentiment analysis tool for social media monitoring. This app not only browses among millions of social media posts (eg. tweets) to find those related to a selected keyword, but also is able to determine the emotion behind them, i.e. recognize if the author’s attitude toward the topic is positive, neutral or negative. Would you like to know how is your brand’s reputation on social media? With this tool it’s a piece of cake.
  • QAcheck: regression testing made simple with Machine Learning. This open-source tool developed by Codete automates the QA process by tracking all visual changes and comparing them to the previous version of the application. As a result, any unexpected styling changes are detected instantly, therefore replacing the burdensome manual UI testing. 

 

Are you wondering what other state-of-the-art ML solutions our experienced developers are capable to create? Reach out to us and let’s talk about how Codete can help you leverage the possibilities of AI and the recent machine learning trends! 

karol.przystalski

Karol Przystalski is CTO and founder of Codete. He obtained a Ph.D in Computer Science from the Institute of Fundamental Technological Research, Polish Academy of Sciences, and was a research assistant at Jagiellonian University in Cracow. His role at Codete is focused on leading and mentoring teams.