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Sometimes reality is a cold slap in the face. Consider, as a particularly salient example, a recently published article concerning the use of artificial intelligence (AI) in the creation of chemical and biological weapons (the original publication, in Nature, is behind a paywall, but this link is a copy of the full paper). Anyone unfamiliar with recent innovations in the use of AI to model new drugs will be unpleasantly surprised.

Heres the background: In the modern pharmaceutical industry, the discovery of new drugs is rapidly becoming easier through the use of artificial intelligence/machine learning systems. As the authors of the article describe their work, they have spent decades building machine learning models for therapeutic and toxic targets to better assist in the design of new molecules for drug discovery.

In other words, computer scientists can use AI systems to model what new beneficial drugs may look like for specifically targeted afflictions and then task the AI to work on discovering possible new drug molecules to use. Those results are then given to the chemists and biologists who synthesize and test the proposed new drugs.

Given how AI systems work, the benefits in speed and accuracy are significant. As one study put it:

The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design.

Specifically, AI gives society a guide to the quicker creation of newer, better pharmaceuticals.

The benefits of these innovations are clear. Unfortunately, the possibilities for malicious uses are also becoming clear. The paper referenced above is titled Dual Use of Artificial-Intelligence-Powered Drug Discovery. And the dual use in question is the creation of novel chemical warfare agents.

One of the factors investigators use to guide AI systems and narrow down the search for beneficial drugs is a toxicity measure, known as LD50 (where LD stands for lethal dose and the 50 is an indicator of how large a dose would be necessary to kill half the population). For a drug to be practical, designers need to screen out new compounds that might be toxic to users and, thus, avoid wasting time trying to synthesize them in the real world. And so, drug developers can train and instruct an AI system to work with a very low LD50 threshold and have the AI screen out and discard possible new compounds that it predicts would have harmful effects. As the authors put it, the normal process is to use a generative model [that is, an AI system, which] penalizes predicted toxicity and rewards predicted target activity. When used in this traditional way, the AI system is directed to generate new molecules for investigation that are likely to be safe and effective.

But what happens if you reverse the process? What happens if instead of selecting for a low LD50 threshold, a generative model is created to preferentially develop molecules with a high LD50 threshold?

One rediscovers VX gasone of the most lethal substances known to humans. And one predictively creates many new substances that are even worse than VX.

One wishes this were science fiction. But it is not. As the authors put the bad news:

In less than 6 hours ... our model generated 40,000 [new] molecules ... In the process, the AI designed not only VX, but also many other known chemical warfare agents that we identified through visual confirmation with structures in public chemistry databases. Many new molecules were also designed that looked equally plausible. These new molecules were predicted to be more toxic, based on the predicted LD50 values, than publicly known chemical warfare agents. This was unexpected because the datasets we used for training the AI did not include these nerve agents.

In other words, the developers started from scratch and did not artificially jump-start the process by using a training dataset that included known nerve agents. Instead, the investigators simply pointed the AI system in the general direction of looking for effective lethal compounds (with standard definitions of effectiveness and lethality). Their AI program then discovered a host of known chemical warfare agents and also proposed thousands of new ones for possible synthesis that were not previously known to humankind.

The authors stopped at the theoretical point of their work. They did not, in fact, attempt to synthesize any of the newly discovered toxins. And, to be fair, synthesis is not trivial. But the entire point of AI-driven drug development is to point drug developers in the right directiontoward readily synthesizable, safe and effective new drugs. And while synthesis is not easy, it is a pathway that is well trod in the market today. There is no reasonnone at allto think that the synthesis path is not equally feasible for lethal toxins.

And so, AI opens the possibility of creating new catastrophic biological and chemical weapons. Some commentators condemn new technology as inherently evil tech. However, the better view is that all new technology is neutral and can be used for good or ill. But that does not mean nothing can be done to avoid the malignant uses of technology. And there is a real risk when technologists run ahead with what is possible, before human systems of control and ethical assessment catch up. Using artificial intelligence to develop toxic biological and chemical weapons would seem to be one of those use-cases where severe problems may lie ahead.

Here is the original post:
Artificial Intelligence and Chemical and Biological Weapons - Lawfare - Lawfare

Artificial Intelligence

Too many technologist, in every generation of technology, state that management need to think more like programmers. Thats not the case. Rather, the technology professionals need to learn to speak to management. The Business Case for AI, by Kavita Ganesan, PhD, is a good overview for managers wishing to understand and control the complexities of implementing artificial intelligence (AI) systems in businesses.

Im always skeptical of self-published books. Usually that means the books just arent that good. However, sometimes, especially in non-fiction, it means that publishers are clueless about the subject and hesitant to work with people who arent names. This book is an example of the second option, and it will give management an introduction to the concepts surrounding AI and how to address implementation in a way that will increase the odds of success for AI initiatives.

The indication that the author mostly lives in the real world comes quickly. The first chapter is a good, introduction to what matters for business about AI. Forget the technical focus, its about solving problems in an efficient and cost-effective way.

Chapter 2, What is AI? isnt bad either, though I disagree with the idea that machine learning (ML) is part of AI. Business Intelligence (BI) has advanced, along with computing performance, that standard analytics provide insight that can be termed ML, so ML and AI overlap. That, however, is a religious argument and what Ganeson has to say about AI is at a good level for management understanding.

The weakest chapter in the introduction is the fourth, where the science fiction addict in me had to sigh at Movies such as I Robot. Ummm, check your library.

That chapters list of myths is also a bit problematic. The first, about job loss, is the one area where it shows the part of the real world in which the author exists isnt one most people are in. The AI revolution is very different than the industrial revolution and earlier technology revolutions. She talks about artificial general intelligence (AGI) and says that since its still far away that means a lot of jobs wont be lost. We dont need AGI to replace jobs.

The next couple of chapters are good for setting up examples of business processes that could be impacted by AI. I do have an issue with which companies she decides to name and which remain anonymous, as that seems to imply protecting customers. The best part was a good discussion of IT & manufacturing operations, but that could have been improved by discussing infrastructure operations such as pipelines and the electrical grid.

Part 3 (chapters 7-9) is very good but, again, has a few things to keep in mind. On page 117, six phases of the development lifecycle are defined. I agree with them, but want to point out that data acquisition and model development, phases 2 & 3, can be done somewhat in parallel. The things you learn from each can impact the other. The other nit is that the author seems to use warehouse improperly. Data warehouses have a specific, more narrow purpose. When she uses the term, think data lake. The importance of logging, transactions and more, is often ignored, and the end of this section of the book has a good explanation of its importance.

The fourth part of the book is a set of chapters that drills down into the finding AI projects portion of the analysis process, and is well laid out.

The final section has two chapters. The first is about build v buy. It is no surprise that a consultant leans towards build, thats her livelihood. What managers need to understand is that businesses arent as unique as they wish to think. There are unique things, but the vast majority of business is like other businesses. AI is a new technology and there arent enough easy to use tools for a buy decision in many areas, but that will change over time. Managers need have a flexible understanding of the equation and balance that in the real-world.

The final chapter is, as expected, a good summation and a return to focusing on business results. It continues the authors use of good, simple, graphics to show the points of her arguments. Regardless of the issues Ive mentioned above, the book does a great job of laying out the challenges of artificial intelligence from a business perspective. The book doesnt delve deeply into algorithms or other details that dont matter to management, while it does provide a framework to look at AI projects through a business lens that integrates the technology into organizations in a way that doesnt leave everything to technologist. The Business Case for AI is a good introduction for IT and line managers to think about how to integrate artificial intelligence into their organizations.

More here:
The Business Case For AI Is A Good Management Introduction To Real-World Artificial Intelligence - Forbes

MANHATTAN BEACH, Calif.--(BUSINESS WIRE)--Endoluxe and Optimus-ISE are proud to announce that they have entered into a co-marketing and development agreement to realize the advanced technology synergies of both organizations. With Optimus-ISE focused on safer, more efficient, and improved financial performing operating rooms, the Endoluxe platform fits perfectly into these guiding principles to provide an optimal clinical environment.

We are thrilled to enter this new global partnership. The Endoluxe platform, consisting of a wireless camera, cloud-based storage, and AI/ML clinical applications, is a fantastic fit with the vision of the Optimus operating room. says Devon Bream, CEO of Endoluxe. Our product eliminates the cables and cords of legacy camera platforms, which aligns with the clutter free design of the Optimus-ISE operating room. Additionally, Endoluxe provides a cloud-based storage solution that eliminates antiquated recording boxes and seamlessly connects to hospital EMRs. The Endoluxe cloud lets clinicians immediately share images from a procedure with patients and family, increasing patient satisfaction experiences. But one of the most exciting opportunities to collaborate with Optimus-ISE is through our novel AI/ML Endoluxe applications that provide clinicians with insights that legacy camera platforms simply cannot offer.

The Endoluxe EVS is the perfect camera system for all endoscopic procedures that utilize industry standard rigid and flexible analogue scopes such as urology, gynecology, ENT, general surgery, and orthopedics. The handheld Orb replaces the legacy endoscopic tower with advanced, portable technology at 1/6 the cost.

We are excited to enter into a partnership with Endoluxe, states Bill Passmore, CCO of Optimus. The Endoluxe co-founders are both practicing surgeons, which adds yet another validation that Optimus-ISE is designing advanced solutions that are meaningful to those that will ultimately be using them. While Optimus remains vendor agnostic, the advantage of collaborating with innovative technologies like Endoluxe allows us to provide our customers integrated options that no other providers can. The potential for collaboration and co-development is vast with both organizations benefiting from shared resources, sales platforms and becoming greater than the sum of the parts with a great cultural fit.

Endoluxe is a world-class endoscopic video imaging organization based in the United States with worldwide distribution of its medical industry design award-winning Endoluxe Orb. The company is focused on reducing costs of legacy video platforms, enhancing procedure adoption, and improving patient outcomes through better therapy application. Endoluxe is committed to being a vendor agnostic platform that allows customers to utilize their existing investment in traditional scopes and supporting devices, while taking advantage of future technological advancements utilizing our portable, integrated, and feature-laden platform at 1/6th the cost of legacy products. More information can be found at Endoluxe.com.

Optimus Integrated Surgical Environment AG is a Swiss-based company that delivers a holistic solution for the entire operating room and surrounding support services. Optimus integrates all vendors by acting as the single supplier for planning, installation and maintenance services for new hospital and refurbished operating room facility builds. The company provides services for the entire lifecycle of the operating sector of hospitals: from blue-sky phase of new operating room build planning, installation, and project management, through the total time of ownership including maintenance, servicing, and technology updates. More information can be found at Optimus-ISE.com.

Link:
Endoluxe and Optimus-ISE Enter Marketing and Development Agreement to Realize Advanced Imaging and Artificial Intelligence in Advanced Operating Rooms...