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Ibm demonstrates groundbreaking artificial intelligence research using foundational models and generative ai.
AI has already demonstrated its power to revolutionize industries and accelerate scientific investigation. One field of AI research that has made stunning advancements is in the area of foundation models and generative AI, which enables computers to generate original content based on input data. This technology has been used to create everything from music and art to fake news reports.
OpenAI recently showcased the impressive capabilities of artificial intelligence by offering free access to ChatGPT, a state-of-the-art generative transformer model. The move generated widespread media attention and excitement among users, highlighting the massive potential of AI. This demonstration came just three months after the release of ChatGPT to the public.
Faced with the disruptive impact of OpenAI's GPT-3 model, Google and Microsoft were compelled to reveal AI integration plans for their respective search engines. The demonstration of AI's practical and powerful capabilities by OpenAI will no doubt raise the public’s expectations and demand for more advanced AI products in the future. OpenAI's move sparked one of the quickest and most significant disruptions in an industry segment that has ever been witnessed.
It is universally acknowledged that human life is of paramount importance. In this article, we shed light on the life-saving potential of AI by examining its practical applications in the creation of new antibiotics and other scientific AI tools. Innovative use of foundation models and generative AI has the capability to increase revenues, optimize processes, and streamline the creation and accumulation of knowledge, however, it also has the potential to save millions of lives around the world. This discussion aims to increase visibility for the importance of AI’s potential to save lives and highlight the need to expand its development and deployment in these areas.
From simple algorithms to breakthrough advances
Artificial intelligence (AI) had rather simple beginnings in the 1950s. It tackled simple algorithms and mathematical models designed for specific tasks. Much later, in the 1990s, AI research underwent a major shift towards machine learning algorithms that enabled computers to improve their performance by analyzing patterns in data and transferring that knowledge to new applications. This shift gave rise to numerous breakthroughs in the field, including the development of deep learning algorithms that revolutionized areas such as computer vision and natural language processing (NLP). These advances have led to even more new achievements and further expanded the potential of AI.
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Today, AI researchers continue to push the boundaries by developing new algorithms and models that can tackle increasingly complex tasks. AI and the size of models continues to evolve at an unprecedented pace, producing responses that are more human-like and expanding the range of tasks it can perform. Breakthroughs and applications are still being made in areas such as natural language processing (NLP), computer vision, and robotics. Despite its limitations and challenges, AI has proven to be a transformative force across a wide array of industries and fields, including healthcare, finance, transportation, and education.
Cutting-edge AI research by an IBM Master Inventor
IBM has one of the largest and most well-funded AI research programs in the world and I recently had the opportunity to discuss its program with Dr. Payel Das, principal research staff member and manager at IBM Research who is also an IBM master inventor.
Dr. Das has served as an adjunct associate professor in the department of Applied Physics and Applied Mathematics (APAM) at Columbia University. She is currently serving as an advisory board member of AMS at Stony Brook University. Dr. Das received her B.S. from Presidency College in Kolkata, India, and her M.S. from the Indian Institute of Technology in Chennai, India. She was awarded a Ph.D. in theoretical biophysics from Rice University in Houston, Texas. Dr. Das has coauthored more than 40 peer-reviewed publications. She has also received awards from Harvard Belfer Center TAPP 2021 and IEEE open source 2022. She also has a number of IBM awards including the IBM Outstanding Technical Achievement Award (the highest technical award at IBM), two IBM Research Division Awards, one IBM Eminence and Excellence Award, and two IBM Invention Achievement Awards.
As a member of the Trustworthy AI department and the generative AI lead within IBM Research, Dr. Das is currently focused on developing new algorithms, methods, and tools to develop generative AI systems that are created from foundation models.
Her team is also working on using synthetic data to make the AI models more trustworthy and to ensure fairness and robustness in downstream AI applications.
The power of synthetic data and how it advances AI
In our data-driven era, synthetic data has become an indispensable tool for testing and training AI models. This computer-generated information is cost-effective to produce, comes with automatic labeling, and avoids many of the ethical, logistical, and privacy challenges associated with training deep learning models on real-world data.
Synthetic data is critical for business applications as it offers solutions when real data is scarce or inadequate. One of the key advantages of synthetic data is its ability to be generated in vast quantities, making it ideal for training AI models. Furthermore, synthetic data can be designed to encompass a diverse range of variations and examples, leading to better generalization and usability of the model. These attributes make synthetic data an indispensable tool in the advancement of AI and its real-world applications.
It is crucial that the generated synthetic data adheres to user-defined controls to ensure it serves its intended purpose and minimizes potential risks. The specific controls required vary depending on the intended application and desired outcome. Ensuring that synthetic data aligns with these controls is essential to guarantee its effectiveness and safety in real-world applications.
Transforming the future with universal representation models
Transformers have been widely adopted for many different applications and proven to be highly ... [+] effective for processing complex data such as natural language
The first AI models utilized feedforward neural networks, which were effective in modeling non-sequential data. However, they were not equipped to handle sequential data. To overcome this limitation, recurrent neural networks (RNNs) were developed in the 1990s, but it wasn't until around 2010 that they saw widespread implementation.
This breakthrough in technology expanded the capabilities of AI to process sequential data and paved the way for further advancements in the field. Then another type of AI model called a transformer, radically improved AI capabilities.
The transformer made its first appearance in a 2017 Google research paper that proposed a new type of neural network architecture. Transformers also incorporated self-attention mechanisms that allowed models to focus on relevant parts of an input and made more accurate predictions.
The self-attention mechanism is a defining feature that sets transformers apart from other encoder-decoder architectures. This mechanism proves especially beneficial in natural language processing as it enables the model to grasp the relationships between words in a sentence and recognize long-term dependencies. The transformer accomplishes this by assigning weights to each element in the sequence, based on its relevance to the task. This way, the model can prioritize the most crucial parts of the input, resulting in more context-aware and informed predictions or decisions. The integration of self-attention mechanisms has greatly advanced the capabilities of AI models in natural language processing.
According to Dr. Das, in recent years, there has been a shift away from RNNs as the primary architecture for natural language processing tasks. RNNs can be difficult to train and can suffer from vanishing gradient problems, which can make it challenging to learn long-term dependencies in language data. By contrast, transformers have been shown to be more effective in achieving state-of-the-art results on a variety of natural language processing tasks.
Unlocking the power of foundational models
Models that are trained using large-scale data and self-supervision techniques can produce a universal representation that is not specific to any particular task. This representation can then be utilized in various other applications with little to no further adjustment.
These models are referred to as "foundational models," a term coined by Stanford University in a 2021 research paper. Many of today's foundational models adopt transformer architecture and have proven versatile in a broad range of natural language processing (NLP) tasks. This is due to their pre-training on vast datasets, which results in powerful machine learning models ready for deployment. The use of foundational models has greatly impacted and improved the field of NLP.
Dr. Das and the IBM research team have been involved in a significant amount of AI research with foundation models and generative AI.
The above graphic shows how a foundation model can be used to build models for different fields by using text as the input data. They may or may not use transformer architecture. On the left side of the graphic, a large language model is shown, which progressively maps letters to words to sentences and finally to language.
The illustration on the right side of the graphic depicts a chemistry transformer model, which connects atoms to molecules and to chemistry. The same concept could be applied to build foundation models for biology or other related fields by representing biological or chemical molecules as text.
It's crucial to note that the transformer architecture is adaptable to a diverse array of fields, as long as the input data can be expressed in textual form. This versatility makes the transformer architecture a valuable tool for creating machine learning models in many domains.
Pushing the boundaries of creativity with generative AI
Generative models have the ability to create new and unique images, audio, or text for a variety of applications. These models have also enabled AI systems to become more effective at processing complex data and have opened up new possibilities for using AI in a wide range of applications.
Foundation models serve as a strong basis for creating generative models due to their ability to handle and learn from vast amounts of data. By adjusting the parameters of these models to focus on a specific task, like generating images or text, new generative AI models can be created that produce unique content within specific fields.
Dall.E2 byOpenAI
As an illustration, if the objective is to develop a generative AI model for art, a pre-trained foundational model would first be trained on a vast collection of art images. After successful training, it could then be utilized to produce novel and original pieces of art. Above is a sample of art created by an AI program named Dall.E2 in response to a prompt requesting it to generate a painted portrait of a human face, as perceived by AI.
Overcoming the small data challenge in generative AI
Implications of foundation models go well beyond NLP
“When we first started working on generative AI,” Dr. Das said, “it occurred to us that one of our problems was learning from small data for any domain-specific or any industry-specific application.”
Generative AI models require large amounts of data to accurately learn and generate new, similar data. When working with small data sets, the performance and usefulness of these models can be limited. Dr. Das recognizes this challenge and understands that techniques like transfer learning and data augmentation can help improve their performance in these situations.
Despite the challenges posed by small data sets for generative AI models, for each of the domains in the above graphic, a vast amount of unlabeled data exists in businesses. This data provides an opportunity to train custom foundational models, enabling the solution of previously thought unsolvable problems. This aligns with IBM Research's focus on exploring new AI capabilities through generative AI and pushing the boundaries of AI science.
Broad generative AI research
Thrice Revealed
IBM has made significant contributions in each of the domains represented in the image. Their work is so extensive, it is difficult to cover all their achievements in a single article.
Synthesizing antimicrobials with generative AI
AI has the potential to revolutionize various fields and speed up scientific progress. As an example, Dr. Das and her research team have leveraged AI to develop innovative antimicrobials to fight against lethal antibiotic-resistant bacteria.
The Fight Against Superbugs
Antibiotics were first used to treat serious infections in the 1940s. Since then, antibiotics have saved millions of lives and transformed modern medicine. Yet the CDC estimates that about 47 million antibiotic treatments are prescribed each year for infections that don’t need antibiotics.
The overuse of antibiotics is a critical problem because it contributes to the development of antibiotic-resistant infections caused by common bacteria like E.coli and staphylococcus, as well as more dangerous and rare bacteria such as MRSA. These resistant infections are challenging to treat and can result in serious consequences like sepsis, organ malfunction, and death.
When traditional antibiotics are no longer able to effectively kill bacteria, it becomes much more difficult or even impossible to treat and control infections. These antibiotic-resistant bacteria—commonly called superbugs—can spread quickly and cause serious infections, particularly in hospitals and other healthcare settings. Superbugs can also be found in the environment, in food, and on surfaces, plus they can be transmitted from person to person.
It is a serious global health problem. Drug-resistant diseases kill 700,000 people annually around the world; by 2050, that number is expected to rise to 10 million deaths per year.
How bacteria outsmart antibiotics
Bacteria and viruses transform into superbugs through the activation of innate defense strategies that render antibiotics ineffective. These defense mechanisms can involve physical, chemical, or biological processes that safeguard the germs and enable them to escape or counteract danger to their existence. Such processes may produce enzymes that inactivate antibiotics, alter the bacterial cell wall making the organism less responsive to the drugs, or allow the bacteria to obtain genetic information from other bacteria that possess inherent immunity to antibiotics.
Streamlining drug development with AI
The conventional method of creating a new antimicrobial drug is a lengthy and expensive undertaking, frequently taking many years and a hefty sum of money before it can be commercially available. But recent advancements in Artificial Intelligence (AI) are revolutionizing the drug discovery and development process.
By utilizing AI's ability to generate and evaluate numerous possible drug candidates, researchers can swiftly pinpoint the most promising options and concentrate their efforts on them. This streamlines the drug development process, cutting down the time and cost involved and leading to the production of more efficient antimicrobial drugs at a quicker pace.
Overview and 48-day timeline of the research team’s proposed AI-driven approach for accelerated ... [+] antimicrobial design Source: IBM Research, Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics. Nature Biomed. Eng., March 2021
In a collaborative effort between Dr. Das and her team at IBM, as well as other organizations, they conducted a study to find innovative solutions to the problem of antimicrobial resistance. The study utilized AI to synthesize and evaluate 20 unique antimicrobial peptide designs, chosen from a pool of 90,000 sequences.
The AI models were specifically designed to combat antibiotic resistance, incorporating controls for broad-spectrum efficacy and low toxicity, and slowing down the emergence of resistance. This approach aimed to create effective solutions that not only fight against resistant bacteria but also minimize the risk of harmful side effects and prevent further resistance from developing.
The team tested these designs against a diverse range of gram-negative and gram-positive bacteria, which led to the identification of six successful drug candidates. The toxicity of these candidates was further evaluated in both a mouse model and a test tube.
AI-powered success
Dr. Das expressed excitement about the success of the design, pointing out that it embodies many of the sought-after characteristics expected in the next generation of drug candidates. The accompanying illustration outlines the plan and estimated duration of using AI to speed up the antimicrobial design process, which can be accomplished in just one and a half months, significantly quicker than the conventional method that takes several years.
The use of AI in accelerating the discovery of new antimicrobial drugs has proven to be a game-changer, offering clear benefits such as faster speed and reduced expenses. Moreover, AI models offer a more streamlined approach by directing the attention of researchers to the most promising leads. Additionally, generative AI enables scientists to design innovative drug compounds that boast unique features and elevated efficacy compared to existing drugs.
The researchers at IBM have harnessed the power of generative AI to streamline the development of new antimicrobial drugs. Additionally, they have used AI to create valuable tools, such as MolFormer and MolGPT, for predicting the properties of chemical molecules which plays a crucial role in various fields including drug discovery and material design.
Wrapping up
Generative AI has captured the attention of various industries, including music, art, healthcare, and pharmaceuticals, as one of the most exciting advancements in AI in recent times. Despite its limitations and challenges, AI continues to demonstrate its potential to revolutionize different fields.
Its ability to swiftly create and test life-saving medicines for antibiotic-resistant bacteria and other pathogens is a testament to its significance and promise.
With the recent buzz surrounding OpenAI's GPT-3 trial and the subsequent developments by Google and Microsoft, it's likely we will not only see a surge in AI-powered products in the coming year, I expect further disruptions to occur. Some may be trivial, but the hope is that many will feature meaningful integrations of AI that will be beneficial to the markets.
Analyst Notes:
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If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.
This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.
Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.
Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.
Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).
Join Lareina Yee and Roger Roberts on Tuesday, July 30, at 12:30 p.m. EDT/6:30 p.m. CET as they discuss the future of these technological trends, the factors that will fuel their growth, and strategies for investing in them through 2024 and beyond.
Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.
The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.
Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.
As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.
Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).
Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.
In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.
Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.
Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.
The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.
Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.
Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.
To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.
What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.
Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.
In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.
The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner in the Washington, DC, office.
They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.
This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.
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