Industry forecasting firm Visiongain released its Antibacterial Drugs Market Report 2024-2034 today, which projected the antibacterial drug market to grow at a compound annual growth rate of 3.6 percent during the 10-year forecast period.

One reason cited for this growth is the impact of artificial intelligence and machine learning on the drug discovery process. The report highlights the recent discovery of a drug to treat the bacteria Acinetobacter baumannii, which built upon efforts two Co-P.I.s who worked on the breakthrough, MIT professors Regina Barzilay and Tommi Jaakkola, had make to investigate interventions for COVID-19 that were funded by the Digital Transformation Institute.

The new drug was identified from a library of ~7,000 potential drug compounds using a machine-learning model trained to evaluate whether a chemical compound will inhibit the growth of A. baumannii. Once approved, the drug could help combat Acinetobacter baumannii found in hospitals that leads to pneumonia, meningitis, and other serious infections.

See the Visiongain report summary here.

See our news brief on the drug discovery study here.

Image: CDC

In its yearly report of the global economy’s most compelling facts and figures, the Atlantic Council includes specs on Generative AI among its 26 highlights, as described by Giulia Fanti, assistant professor of electrical and computer engineering at Carnegie Mellon University, a nonresident senior fellow at the Council’s GeoEconomics Center, and also a Principal Investigator on cybersecurity for the Digital Transformation Institute.

Fanti brings to light the staggering parameters in state-of-the-art Large Language Models (LLMs), followed by her explanation.

This year, generative artificial intelligence (AI) captured the public’s imagination with its ability to generate photorealistic images, videos, audio, and text. Many believe that models such as GPT-4, PaLM 2, Llama 2, and Mistral will revolutionize how humans interact with computers for government services, education, and enterprise settings, to name a few. However, the amazing capabilities of generative models come at a cost.

Today, the leading models are growing quickly in size (as measured by their number of parameters, the values that control LLMs’ behavior). This matters because larger models are more expensive to train and more expensive to use once trained. For example, the Llama 2 (70B) model has 70 billion parameters and required a staggering 1.7 million graphics processing unit (GPU) hours, or the equivalent of almost two hundred years, to train. (This was sped up in practice by using these resources in parallel.)

The geoeconomic implications of these trends are likely to become more severe in the coming years. To train or host these models, organizations will need access to data centers with many GPUs. Moreover, due to data use and data locality restrictions in many regions, such data centers may need to be local. However, data centers are distributed inequitably across the world, with the vast majority of data centers located in the United States and Europe. This is likely to lead to a massive disparity in the ability to train, use, and benefit from generative AI.

Read the full story here.

Atlantic Council image: Mark Schiefelbein/Pool via REUTERS

August 31, 2023

The Digital Transformation Institute releases its 2022-2023 Annual Report, including a retrospective of the most impactful programs since the institute’s launch in March 2020. Co-directors S. Shankar Sastry of the University of California, Berkeley and R. Srikant of the University of Illinois Urbana-Champaign contribute the forward to the report. Readers can view and download the 32-page DTI 2022-2023 Annual Report pdf.


We began the Digital Transformation Institute three years ago with the goal of developing the scientific foundation of the Digital Transformation of Societal Systems. Technologies at the nexus of AI/ Machine Learning, Internet of Things, and Data Analytics were creating opportunities for new business models and services in societal-scale systems, including transportation, energy, health care, and finance.

Through our research grant program, we funded three rounds of proposals in areas ranging from health care and COVID-19 to distributed energy utilization and mitigating climate change to new defenses against cyber threats on financial and cyber-physical infrastructures. The 72 funded projects have already borne fruit. The pandemic accelerated the pace of digital transformation, additionally stimulating our research programs and impact.

DTI-funded research has resulted in the discovery of novel vaccines for COVID-19 with implications for cancer research, advanced epidemiological models for the spread of contagion, and new algorithms to prevent the kinds of blackouts that struck Texas in 2021. From DTI research, new techniques for monitoring emissions and leaks of hydrocarbons have emerged, along with early wildfire-warning systems, and AI-powered forecasts predicting hurricanes and El Niños soon enough to prepare, evacuate, and save lives. Our projects have developed new defenses against advanced persistent threats and data integrity attacks targeting financial infrastructures.

Our researchers have made new uses of the C3 AI Suite and cloud computing platforms for novel drug development, optimizing both crop yield and carbon capture in agriculture, and hardening electric power systems. Innovative DTI energy projects include work on an advanced electrode to capture “forever” chemicals and machine learning algorithms to navigate floating solar-powered kelp farms over tides to deposit sequestered carbon dioxide in deep ocean channels.

It is fair to say that from the immense volume of creative output that has been published, and presented at our colloquia and deep-dive workshops, a new discipline of the Science and Technology of Digital Transformation is taking shape. This new discipline combines machine learning and AI, data science, mathematical economics, and mechanism and incentive design into new normative models for introducing, assessing, and implementing new products and services. We expect these programs to grow rapidly in number and interest, at both business and engineering schools.

The work is both normative and behavioral, in that the robust algorithms that we have developed adapt to changes in human patterns and practices. It is well-known that societal systems change and adapt to new digital transformation services and solutions. What is novel is how to incorporate these changes into the designs of algorithms integrating cognitive models of human decision-making.

The emergence of Generative AI and its methods for going beyond analytics to generate intelligent insights and predictions about the future will further advance this science. The ability for AI systems to work with humans, learn their reasoning styles, and adapt to provide meaningful advice is the starting point for well-integrated Human-AI teams. Generative AI has already shown what it can do in terms of generating text, images, and even software code. For use in societal systems, we will need to provide trust and high confidence guarantees about its conclusions.

This is an exciting intellectual agenda, and looking to the future we feel that DTI is ideally positioned to take on the challenges of integrating tools such as Generative AI into Digital Transformation solutions. 

S. Shankar Sastry, University of California, Berkeley
R. Srikant, University of Illinois Urbana-Champaign
Co-Directors, Digital Transformation Institute Digital Transformation Institute 2022-2023 Annual Report