Generative AI is quickly becoming a reality. Global investment in AI has grown from $12.75 million in 2015 to $93.5 billion in 2021, and the market is expected to reach $422.37 billion by 2028. Already more than 2 billion dollars have been invested in generative AI, up 425% since 2020, according to the Financial Times.
Generative AI refers to machine learning algorithms that can create new meaning from text, images, code, and other forms of content. The main generative AI tools are: Alpha Code from DeepMind (GoogleLab), ChatGPT from OpenAI, GPT-3.5, DALL-E, MidJourney, Jasper and Stable Diffusion, which are great language models and image generators .
In my last Forbes article, I wrote extensively about ChatGPT which Elon Musk says is a scary product and takes us into a dangerous AI zone, and I also featured Jasper, a competitive chatbot at Open’s ChatGPT Mind.
This article will focus on Google (Alphabet), Deep Mind’s Alpha Code which is a new AI system for developing computer code, which can achieve average human level performance in solving programming contests.
What will happen when we have large AI models of neural networks capable of generating more complex problem-solving and critical thinking skills?
Alpha Code, like ChatGPT, uses self-supervised learning and an encoder and decoder architecture to solve natural language problems by predicting code segments based on the previous segment and generating millions of viable solutions, then classifies and recommends the ten best possible solutions. DeepMind also claims that AlphaCode has achieved an average top-performing 54.3% ranking across 10 recent contests with over 5,000 entrants each. I expect that in 2023, the average ranking will exceed 65%, because the more code he learns will only improve in quality. By 2030 – we could see levels of predictive accuracy over 90% – then many realities will change again in the life of a software developer.
Will DeepMind’s Alpha code impact future developer demand – probably not in the next decade, but 15 years from now as these systems continue to improve and get smarter – I am 100% convinced.
This can be a very good development as it opens up the world of coding to be more accessible to more knowledge workers to advance AI systems and help humanity evolve.
DeepMind researchers stress that their work is far from threatening human programmers, but that its systems must be able to develop problem-solving capabilities to help humanity. Our exploration of code generation leaves plenty of room for improvement and hints at even more exciting ideas that could help programmers improve their productivity and open the door to people who don’t currently write code. (Source: ZDNet News)
According to the University of Cambridge, at least half of developer effort goes into debugging software code, costing the software industry an estimated $312 billion a year. AI-powered code suggestion and review tools promise to cut development costs while allowing coders to focus on creative, less repetitive tasks — assuming systems work as advertised. When I think of our own business of designing and building AI software products for our clients, and the testing and debugging effort, the appeal of software solutions like DeepMind’s Alpha Code is very appealing – no not from a downsizing perspective, but from the ability to put more software resources into building new functions and features and let mundane bots do the cleaning – or the mess, and give engineers more interesting tasks to perform.
As generative AI continues to evolve, we are entering a new frontier in the evolution of the AI industry. We will advance and accelerate the discovery of new drugs and advance medical research. Gartner predicts that by 2025, at least 30% of all new drugs and materials discovered will come from generative AI models.
We’re already seeing tools like GPT-3 and ChatGPT use AI in innovative ways in terms of text and natural language, so it’s nearly impossible to know what human-created content and responses are. against an AI-generated response. With highly repeatable processes, where humans don’t have much variety in their communication responses – such as in call centers, administration functions, routine medical questions, etc. – these new chat innovations will begin to impact a variety of knowledge worker roles.
Although generative AI is in its infancy and global legislation is not yet aligned with AI ML governance from a regulatory/legal law perspective, 2023 will see a tremendous positive ethical acceleration of AI and it is clearly a need as we are rapidly approaching a world without precedent. in human history – buckle up because 2023 will bring many new possibilities and sadly even more cybersecurity risks, as the world of deep counterfeits will only grow stronger with more intelligence activations from the Generative AI.