2023 was a year marked by exponential growth in the tech sector, especially where Artificial Intelligence (AI) is concerned. Open AI released ChatGPT and it went viral. Google introduced Bard. Microsoft revealed its version called CoPilot. Anthropic unveiled Claude. These may be some of the front runners but numerous others are scampering for a spot at the AI table.
Several organizations are now testing how AI might help them work smarter and not harder. BP is placing more emphasis on using generative AI into their organizational culture said Justin Lewis, Vice President of Incubation and Engineering at BP. Lewis said that they are already noting that their employees are performing about ten times better with the assistance of AI. That’s pretty impressive! BP constructed a team to review codes using AI and now one engineer can review that code from one hundred seventy-five (175) other engineers with ease. Not bad eh?
Nothing is always smooth sailing. There will be hiccups along the way with the use of AI especially in the workplace. Managing how your employees use AI technology and tracking anything that may be inappropriate or puts the organization at risk is called corporate governance.
Generative AI uses a variety of information to construct their models including intellectual property. Speech-to-text is improving but not perfect yet. Large language models would be able to personalize this by using higher-level actions and that would be a vast improvement. More personalization means less prompting and intervention from the users. Currently, most generative AI requires several prompts and user mediation. Organizations are seeking guidance from government regulators in this regard.
Hallucinations are also an issue. Multimodal and open models may remedy some of this hallucinogenic data output. Multimodal models piece together differing modes and types of data. It can easily create images, audio, or video from text inputs. Dall-E and Google Gemini are both examples of this.
Most in the loop believe that quantum computing will master as many technological leaps as AI has and they are probably correct in that assumption. IBM has created what they call System Two and it is capable of merging three processors so they can work in concert. Now they are gearing up to see how they can create value in this enterprise across the board.
Quantum computing is different than how we use our classical computers. Classical computers have sufficed for a long while now and can run simulations. Experts agree that you do not want to run quantum questions on a classical computational foundation. For questions or problems that are more difficult to solve, we can use quantum computers. They can do so much more mathematically and in a far shorter time.
Quantum computing might be able to take off in the areas of nature simulation, i.e. materials, chemistry, and properties where they might most benefit healthcare and life sciences; complex structural data where classical computers get stuck; and logistical optimization which could help with an array of industries from space exploration to risk management, and maybe even into the area of financial investment optimization in portfolio management.
Instead of seeing generative AI and quantum computing at odds let’s see how they can work together to produce more credible data and create more efficiency. Generative AI can effectively do a lot of things faster and better than most humans are capable of. Quantum computers take AI a step further. They can manage complex tasks more rapidly and reliably than any AI could do at this point. Therefore, if we merged them like an Oreo cookie the quantum computer could use the models from generative AI and help generative AI to learn and create considerably faster too. It’s all a matter of training.
Training models for generative AI take days or weeks to complete now. They are trained on classical computers using data patterns to solve complex structural problems through machine learning.
Quantum computing considers many diverse disciplines. These range from mathematics and physics to computer science. Quantum mechanics is what they use to resolve complex problems that our classical computers cannot and in record time. Instead of days or weeks, information could be available within hours or minutes with the assistance of quantum computers. Now that’s progress!
Quantum computers could be used to train generative AI. The time savings alone could make an investment in quantum computing worthwhile.
Your organization will have to weigh the benefits against the cost to see whether quantum computing is for them. In the interim generative AI can help streamline many organizational tasks by curbing inefficiencies, increasing output, and helping you to make better decisions moving forward.
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