Top 5 trends in AI
I don’t think anyone could have predicted all the buzz around OpenAI, which made 2022 the year of generative AI. But the speed of progress in the field of artificial intelligence shows no signs of slowing down. In this article, I will explore the top 5 trends in artificial intelligence as I see them, highlighting the key developments and innovations that are moving this still promising area forward. From Generative AI to Quantum machine learning, these trends are shaping the future of artificial intelligence.
Generative AI
While traditional artificial intelligence systems are designed to perform specific tasks or recognize patterns in existing data, generative AI is capable of creating new content such as text, images, video or audio.
The main feature of generative AI is that it does not copy the existing data on which it was trained, thus is not limited to them. This makes it particularly useful for tasks such as copywriting, where the system can generate new human-like versions of the text that are not just literal copies of the source.
Some cases of generative AI real-life applications include:
- OpenAI, which uses generative AI to develop machine learning algorithms that can generate human-like text. A prime example of this technology is chatGPT, which has created a lot of buzz in recent weeks.
- In finance, generative AI is being used by leading banking structures to automate complex financial processes, f.e. risk management. With generative neural networks, it is possible to create economic scenarios that are useful for predicting the future of financial markets.
- Gradient Music is the first AI music streaming platform. All tracks in Gradient Music were created by artificial intelligence. Despite this there are different “music artists” with different styles on the platform, which means AI can imitate various genres. This brings gradient music closer to real streaming services with real artists.
The technological stack of generative AI is similar to that of traditional AI, in that it typically involves the use of machine learning algorithms and techniques such as deep learning. But the fact that generative AI is focused on generating new content, requires the use of more advanced techniques, such as variational autoencoders and generative adversarial networks, which are specifically designed for content generation.
As technology evolves, generative AI has the potential to make a content revolution various industries by enabling the creation of new content. Venture capital investment in generative AI has increased 425% since 2020 to $2.1bn this year. Therefore, although almost everyone is talking about generative AI in 2022, this is just the beginning.
Use of artificial intelligence in healthcare
AI significantly increases the accuracy and efficiency of many medical processes.
One of the key applications of artificial intelligence in healthcare is the development of personalized treatment plans. By analyzing the patient’s medical history and other relevant data, artificial intelligence systems are able to determine the most effective treatment options and provide personalized recommendations. This can improve patient outcomes and reduce the risk of adverse reactions to treatment.
For example, PandaOmics, an AI-driven platform developed by Insilico Medicine, was used to analyze datasets related to DNA repair disorders to find new biomarkers capable of stratifying cancer patients with different survival outcomes.
Artificial intelligence is also used to improve the effectiveness of drug discovery and development. By analyzing large amounts of data on the effects of various compounds on the human body, artificial intelligence systems are able to identify potential new drugs and predict their likely effects. This can reduce the time and cost of developing new drugs, as well as improve their safety and effectiveness.
For example, in 2020, Exscientia has already discovered the first drug with the help of artificial intelligence — a drug for the treatment of the obsessive-compulsive disorder (OCD), however it has not passed clinical trials yet. But there are already 23 AI-driven drug candidates in clinical trials as of August 2022, according to a recent study. Keep in mind that clinical trials are a long process that can take 10 to 15 years and cost billions of dollars.
AI coding assistants
AI coding assistants are tools to help programmers write, debug, and optimize code. These tools have the potential to increase the efficiency and productivity of software development by automating many tedious and time-consuming coding tasks.
AI coding assistants can provide a wide range of benefits, including the ability to:
- Automatically complete code based on the programmer’s intentions
- Identify and fix errors in code
- Suggest alternative approaches to improving the performance of code
- Generate documentation and other supporting materials
- Analyze code to identify potential security vulnerabilities
Some examples of AI coding assistants include:
- Kite uses machine learning to provide code completion suggestions in real-time as the programmer types.
- MutableAI can complete your code using just natural language.
- IntelliCode is a feature of the Visual Studio IDE that uses AI to provide code completion suggestions based on the context of the code being written.
AI coding assistants have enormous potential, and they are likely to become an important tool in the software development process.
Explainable AI
The black box problem of AI refers to the inability of many AI systems to provide clear and understandable explanations for their decisions and actions. This lack of transparency and interpretability can make it difficult to understand how AI systems make decisions, and can also make it difficult to identify and correct errors or biases in AI systems.
Explainable AI refers to the ability of a machine learning model to provide explanations for its predictions and decisions. This can be achieved using techniques such as linear regression, decision trees, and random forests. For example, SHapley Additive exPlanations (SHAP) and LIME are explainability tools that allow you to explain the decisions made by a machine learning model using local interpretations.
Here is AI in action. AI-powered autonomous vehicles have the ability to sense their environment with high precision and make safe, real-time decisions. However, it is important for these vehicles to also be able to explain their decision-making process to humans in order to gain trust and comply with regulations. These explanations can come in the form of visual explanations, which show which parts of an image influenced the AV’s decision, or textual explanations, which provide a natural language explanation for the AV’s actions.
Another case relates to risk assessment. For example, AI-powered credit scoring system might explain that a certain applicant has a low credit score because they have a history of late payments and a high debt-to-income ratio.
Bonus: Quantum machine learning
Quantum machine learning is the integration of quantum algorithms within machine learning programs. While machine learning algorithms are used to compute huge amounts of data, quantum machine learning is specialized quantum systems to increase the speed of calculations and data storage performed by algorithms in the program.
There are several promising directions for the future of quantum machine learning, including:
- Developing more accurate models for predicting complex phenomena, such as weather patterns or financial markets.
- Improving the efficiency of machine learning systems by enabling them to solve complex problems more quickly and with less data.
- Developing more powerful and versatile AI systems with the ability to adapt to a wider range of applications and challenges.
- Enabling new applications of machine learning that are currently impossible with classical algorithms, such as simulating quantum systems or analyzing large-scale networks.
Unfortunately, only about 2,000 to 5,000 quantum computers will likely be operational by 2030, and those capable of handling the most complex problems may not even exist until 2035 or later. But researchers all over the world are consistently working on the development of technology. For example, in March 2021, a team of researchers from Austria, The Netherlands, the USA and Germany reported the experimental demonstration that quantum effects help speed up learning with reinforcement, which can take an extremely long time in the classical version. As a result of the experiment, it was possible to accelerate machine learning with reinforcement by 63%
Quantum machine learning has huge potential as a technology. It is unlikely that we will see this in the near future, especially in 2023, but the development of quantum machine learning is inevitable, given the impact of the described directions of technology development.
Instead of a conclusion
The field of artificial intelligence is developing rapidly, and in the coming years, we will witness various fascinating trends. Who knows, maybe even this article was written by some generative neural network 🙂
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