Quantum AI

 

What do you get when you combine the deep learning AI field with quantum computing?  You get into the new realm of increasing computational power exponentially that might one day solve the current unsolvable problems.

As of today, cell phones, computers, and supercomputers operate in a binary system where the classical bit stores information as zero or one.  In quantum computing, a quantum bit (or qubit) can store both zero and one at the same time, a property called superposition.  This special property can process many calculations simultaneously, which allows quantum computing to solve complex problems more efficiently.

qAIntum.ai is a company at the forefront of building large language models (LLMs) with quantum computing. The company’s cutting-edge innovation is the Quantum Transformer Architecture where quantum neural networks (QNNs) are integrated with traditional transformer models.  The company’s objective is to build Quantum Large Language Models (QLLMs) with a potential creation of more sophisticated and accurate language models.

                                                                                                       Image:  qAIntum.ai

Then, there’s the Quantum AI group at Google that has been working on quantum computing for almost two decades.  This group has achieved quantum supremacy in 2019 where computation took just 200 seconds that would have taken the most powerful supercomputers thousands of years to calculate.  In 2023, the group achieved another milestone quantum error correction by showing that it’s possible to reduce errors by increasing the number of qubits.

Whether quantum computing AI is hype or reality, I have to give respect to the brilliant scientists and researchers who dedicate their professional lives to work on these high risk theoretical concepts.  These pioneering researches might not have clear real-world business applications initially, but if succeeded could advance the technological progress for humanity.

AI Planet

 

As AI continues to advance and integrate into the many facets of personal and professional life, it’s important to have some knowledge of this deep learning field in order to stay current.  However, for busy professionals who juggle between life and work, the dilemma is how to carve out the free time for learning.

Introducing the 5-week Data Science bootcamp from AI Planet, a compact bootcamp perfect for busy professionals.  This course is designed to give beginners a good foundation of data science with practical exercises.

AI Planet’s benevolent intention is to democratize data science learning so it provides this bootcamp and other data science courses for free. Taking this bootcamp might not turn you into an AI/ML engineer overnight, but it gives you an excellent foundation from which you can build upon your data science knowledge further.  Good luck!

LQM

Since the initial release of ChatGPT-3.5 in November 2022, Large Language Models (LLMs) have received huge attention as the generative AI model.  Subsequent LLMs such as OpenAI GPT-4o, Meta Llama 3.1, or Anthropic Claude 3 Sonnet continue to generate high interest.

There’s another trend that the next AI wave will include Large Quantitative Models (LQMs), which are designed to handle complex data sets and capture the intricacy of quantitative relationship that LLMs can’t.  You would use LLMs which focus on language tasks to summarize documents, but to discover the next cancer drug you would need LQMs.  LQMs utilize advanced computational techniques and data analysis to make predictions, identify trends, and optimize outputs.  The impact of LQMs will be enormous because these AI models will accelerate new discovery in pharmaceutical drugs, chemical compounds, financial models, weather patterns etc.

Advantages of LQMs:

LQMs have many advantages over LLMs and conventional predictive AI models:

1)  Precision:  LQMs excel in tasks that require numerical precision and the ability to analyze vast quantitative data and to model complex mathematical relationships.

2)  Interpretability:  LQMs address the “black box” nature of AI models by offering enhanced interpretability.

3)  Flexibility:  LQMs can be fine-tuned for specific quantitative tasks, providing powerful tools for data-driven decision-making.

4)  Robustness:  LQMs can understand hidden patterns in data and create new synthetic data to add to the limited historical data, making forecasts and predictions more reliable and accurate.

Overall, LQMs promise to usher in the next wave of deep knowledge exploration and discovery.  LQMs will be critical in fields such as finance, healthcare, energy, supply chain management, climate modeling, manufacturing, and retail.  Some examples of LQMs being used in the industry include Flatiron Health, GE Predix, or Paige.  While LQMs also have challenges, their potential benefits in improving decision-making and optimizing outcomes are substantial.