In the year 2022, the world witnessed an array of revolutionary innovations in the segment of artificial intelligence and machine learning. Corporate giants like Meta, Microsoft, and Google developed crucial advancements decked with AI technology, from generative AI to quantum computing.
One of the most significant breakthroughs which garnered the limelight was HyperTreeProofSearch, also known as HTPS by Meta. It aided in solving the problems from the International Math Olympiad. Similarly, the DeepMind and ESMFold from Alpha Fold and Meta, respectively, were deployed for the purpose of protein fold prediction. Besides, Google also came up with DeepNull, which modeled the association connecting covariate effects between phenotypes and enhanced the Genome-Wide Association Studies or GWAS.
The list of such advancements is long, indicating that there is much to look forward to in 2023 regarding generative AI. This blog accentuates the probable trends for the upcoming year that every AI enthusiast should know!
What is generative AI?
For the unversed, generative AI is the next step in machine learning. It is a process of using algorithms to generate data that humans can use. The models of generative AI are exceptionally versatile, taking in content like images, voice recordings, emails, long text formats, social media content, structured data, and program code. They can provide fresh content, summaries, translations, responses to questions, videos, and sentiment assessments.
4 Generative AI Trends Projected for 2023
In 2021, the valuation of the generative AI worldwide market accounted for $8.12 billion and was projected to touch the mark of $63.05 billion by 2028. A CAGR of up to 33.7% is expected over the forecast duration of 2022 to 2028. The generative AI trend will substantially impact drug discoveries, financial services, the material science industry, and many more. The below-mentioned are a few trends that are likely;
1. Text to Image Generation and More
The text-to-image generation was one of the most sought-after trends of 2022. Enthusiasts were eyeing models such as Midjourney, DALL-E, and Stable Diffusion for experimenting with AI for art generation. Gradually, things progressed from text to image and then text to video. Now 2023 might witness text to anything trend owing to the rapid development of 3D models.
The reason behind the surge in the text-to-anything trend could be the language models advancing along with enhancement in diffusion models. The widely accessible datasets are making the models of generative AI more inflatable.
2. Fine-tuning of SLMs and LLMs
The ChatGPT was applauded for its exceptional; conversational competence. It was engineered on GPT-3 by OpenAI, containing around 176 parameters lying on the bigger scale models. However, many other LLMs exist in the market with 2x, 3x, or even 10x parameters compared to GPT-3. Yet, the models by Meta and DeepMind with just half the number of parameters or SLMs have outpaced GPT-3 on tasks such as prediction and logical reasoning.
Besides the reduction in the models' size, a bigger model, such as GPT-4 decked with over 100 trillion parameters, is predicted to arrive in 2023. This jump could be pretty sizable as the existing largest model is the Switch Transformer Model by Google, with 1.6 trillion parameters. On another note, the refinement of current models for better predictability and latency to serve particular purposes could be more prominent in the upcoming years. For instance, the GPT-3 was fine-tuned by OpenAI through the DaVinci update.
3. Dependability on Supercomputers
The need for supercomputers has skyrocketed generously, owing to an increase in generative models and datasets. Besides, the evolution of FastSaaS has further escalated the demand for higher-performing computers. Recently, Microsoft and NVIDIA collaborated to develop Quantum-2, which is a cloud-driven supercomputing platform.
Following a similar path, Tesla also introduced its first-ever supercomputer, Dojo, created from scratch through Tesla's chips. In the near future, it might become accessible to enterprise consumers too. With the new innovations on the way, 2023 could be a big year for the flourishment of supercomputers.
4. A Combination of Traditional and Quantum Computing
As big tech companies like Microsoft, NVIDIA, and Google extend their hardware assistance to the cloud, more innovations encircling quantum computing might occur. It will enable small-size tech firms to train, examine, and develop the models of AI and ML minus heavy hardware. Other domains, such as financial services and healthcare, could be the major ones to get benefitted from this technology.
In an experiment, a quantum computer was linked to the fastest supercomputer in Europe to amalgamate traditional and quantum computing for the speedier solution of problems. NVIDIA has also launched Quantum Optimised Device Architecture (QODA), which exemplifies hybrid quantum-traditional computers.
What are The Skills Needed for Making a Career in the Generative AI?
Generative AI is a field of study that focuses on designing, implementing, and studying systems that can create new content in response to user input or other stimuli. The most essential AI skills that one requires are data mining, problem-solving, analytical thinking, natural language processing, and data interpretation. Likewise, a background in programming languages such as Python and Java and experience with deep learning models are also necessary.
How to upskill yourself as an AI aspirant?
An AI certification is an excellent way to refine your knowledge regarding the subject and your skills. It also provides a way for employers to find the best-qualified candidates, especially in the case of AI jobs where there are many applicants with similar qualifications.
The Final Words
From reinventing the hybrid computing models to developing smaller chips to run supercomputers, the inventions taking place in the segment of generative AI are innumerable. This pace is set to continue in 2023, with more such innovations by the leading tech companies on the way to reimagine what AI-based technology could do.
