Machine Learning and Artificial Intelligence is a field that is driving major innovations across different industries. It is predicted that the AI market will reach US$500 billion in 2023 and US$1,597.1 billion in 2030. This means that machine learning techniques will be in continuous high demand in the near future.
However, the machine learning industry moves very quickly: new technologies and scientific research define how new products and services are built. At the end of 2022, everyone from machine learning engineers to startup founders is looking for the most promising trends for the next year.
01 Machine Learning Technology Trends
With new innovations emerging every day, we can never predict with 100% certainty what technology demand will be in the next year. But based on what we see in 2022, here are the most promising machine learning trends for 2023.
1.1 Foundation models
Large language models are an important innovation, popular recently, and likely to be with us for a long time to come. A cornerstone model is an artificial intelligence tool that can be trained on vast amounts of data, even compared to regular neural networks.
Engineers want computers to understand not just by searching for patterns, but also by accumulating knowledge to reach new levels of understanding. Keystone models are useful in content generation and summarization, coding and translation, and customer support. Well-known cornerstone model cases include GPT-3 and MidJourney.
One of the amazing things about cornerstone models is that they can also scale quickly and work with never-before-seen data, and thus have excellent generative power. Leading providers of these solutions are NVIDIA and Open AI.
1.2 Multimodal Machine Learning
In tasks such as computer vision or natural language processing that involve a model interacting with the real world, the model can often only rely on one type of data, either images or text. But in real life, we perceive the world around us through many senses: smell, hearing, sight and taste.
Multimodal machine learning builds better models by experiencing facts about the world around us in multiple ways (modalities). "Multimodality (MML)" means building ML models that can perceive events in multiple modalities at once, like humans.
Building an MML can be achieved by combining different types of information and using them in training. For example, matching images with audio and text labels to make them easier to recognize. So far, multimodal machine learning is a very new field, yet to be developed and developed by 2023, but many believe it could be the key to achieving general artificial intelligence.
1.3 Transformers
Transformers are AI architectures that use encoders and decoders to transform a sequence of input data and transform it into another sequence. Many cornerstone models are also built on Transformers. We want to separate them out because they are used in many other applications. In fact, Transformers are reportedly taking the AI world by storm.
Transformers, also known as Seq2Seq models, are widely used in translation and other natural language processing tasks. Because Transformers can analyze sequences of words rather than individual words, they often show better results than ordinary artificial neural networks.
Transformers models are able to assign weights to assess the importance of each word in a sequence. The model then translates it into sentences in different languages, taking into account the assigned weights. Some leading solutions that can help you build Transformers Pipelines are Hugging Face and Amazon Comprehend.
1.4 Embedded Machine Learning
Embedded machine learning (or TinyML) is a subfield of machine learning that enables machine learning techniques to run on different devices.
TinyML can be used in home appliances, smartphones, laptops, smart home systems, and more. As explained by Lian Jye Su, AI&ML Principal Analyst at ABI Research:
The widespread application and daily life of artificial intelligence has promoted the development of Internet of Things (IoT) analysis. Data collected from IoT devices is used to train machine learning (ML) models, generating valuable new ideas for IoT as a whole. These applications require powerful and expensive solutions that rely on complex chipsets.
The growing popularity of embedded machine learning systems is one of the main drivers of chip manufacturing. If ten years ago the number of transistors on a chipset doubled every two years according to Moore's Law, which also allowed us to predict increases in computing power, over the past few years we've seen 40-60% per year leap. We believe this trend will continue in the coming years.
Embedded systems are becoming even more important as IoT technologies and robotics become more widely available. The Tiny ML field has its own unique challenges that are yet to be solved by 2023, as it requires maximum optimization and efficiency while conserving resources.
1.5 Low-code and No-code solutions
Machine learning and artificial intelligence have permeated nearly every field from agriculture to marketing to banking. Making ML solutions accessible to non-technical employees is often cited by managers as key to maintaining efficiency across the organization.
However, rather than go through a lengthy and expensive learning process of programming, it is better to simply choose an application that requires zero or close to zero coding skills. But that's not the only problem a No-code solution might address.
Gartner found that the demand for high-quality solutions in the market is more likely than Deliver - "Deliver these solutions at least 5 times faster than IT capabilities". (Gartner has found that the demand for high-quality solutions on the market is bigger than the possibilities to deliver – “it grows at least 5x faster than IT capacity to deliver them”.)
No-code and Low-code solutions can help bridge this gap and meet needs. Likewise, low-code solutions enable technical teams to come up with and test their hypotheses more quickly, reducing lead times and development costs. If 10 years ago, it took an entire team to build an app or launch a website, today just one person can do the same thing, and fast.
Furthermore, 82% of organizations having trouble attracting and maintaining the quality and quantity of software engineers prefer to build and maintain their applications with the help of No-code and Low-code technologies.
Although many Low-code and No-code solutions have emerged in recent years, the general trend is that they are still of poor quality compared to conventional development. Startups that can improve the status quo are more likely to come out on top in the AI market.
Finally, it’s worth mentioning that cloud computing remains an important enabler of innovation as the computational power required to train ML models grows rapidly, especially for real-time ML running in large organizations. According to statistics, about 60% of the world's corporate data is stored in the cloud, and this number is likely to continue to grow. In 2023, we will see continued increases in investment in cloud security and resilience to meet the growing demands of the ML industry.
02 2023 ML technology field TOP
Gartner has identified the technology areas that are expected to develop the most machine learning in the next 7-8 years. Key areas include:
- Creative artificial intelligence. AI for generating text, code, and even images and videos is gaining traction in 2022, especially with MidJourney releasing SOTA Image Generation Networks, DALLE-2, Stable Diffusion, and Open AI releasing new text- davinci-003. In 2023, products and services that use next-generation artificial intelligence for fashion, creativity and marketing will be in high demand.
- Distributed enterprise management. As remote work becomes the norm, companies must find new ways to manage employees and stay productive. According to Gartner, ML will help distributed companies grow and increase revenue.
- automation. From security to banking, autonomous software systems capable of taking on increasingly complex tasks and adapting to rapidly changing conditions are in high demand across many industries. New innovations that offer smarter automation will emerge in 2023.
- cyber security. With the increasing digitization of all spheres of life and the need to protect sensitive information, the importance of cyber security is increasing day by day. ML and AI are considered to play a vital role in protecting private data and keeping organizations safe.
03 Conclusion
In 2023, machine learning will still be a promising and fast-growing field that will bring many interesting innovations. Large language models, multimodal machine learning, Transformers, TinyML, and No-code and Low-code solutions are emerging technologies that will be very important in the near future.
In 2023, some technology areas that will increasingly use ML are creative artificial intelligence, automated systems, distributed enterprise management, and cybersecurity. Gartner predicts that by 2023, ML will permeate more areas of business, helping to improve efficiency and job safety.
—— Wonderful recommendation——
1. Book recommendation - "Explainable Machine Learning"
2. Book recommendation - "Deep Reinforcement Learning"
3. Bytedance Li Hang: The future of artificial intelligence requires new paradigms and theories