Daily Archives: October 18, 2023

Harvard University Information Technology began a limited rollout of the pilot version of its artificial intelligence sandbox tool on Sept. 4, with the aim of providing Harvard affiliates with secure access to large language models.

The AI sandbox provides a walled-off environment where prompts and data entered into the interface are seen by the user only the data is not shared with LLM vendors and cannot be used as training data for these models, according to a press release on HUITs website.

HUIT designed the tool in collaboration with the Office of the Vice Provost for Advances in Learning, the Faculty of Arts and Sciences Division of Science, and other colleagues across the University. HUIT spokesperson Tim J. Bailey wrote the pilot aims to encourage safe experimentation with LLMs, inform how Harvard can offer increased accessibility of the tools, and explore applications of AI in the classroom and workplace.

Pilot access is being rolled out in phases, with access expanded to instructors within the FAS two weeks ago. Still, affiliates must request access to use the AI sandbox for each specific use case.

Harvard Business School professor Mitchell B. Weiss 99 was one of the inaugural participants in the pilot AI sandbox.

Weiss said the AI sandbox was a crucial educational resource in his course HBSMBA 1623: Public Entrepreneurship. He praised the AI sandbox for offering easy access to a range of generative AI models.

Weiss, by incorporating AI into his course, said he hoped to provide insight into the broader question of, How can generative AI be useful in helping solve public problems?

The interest is spreading as examples of uses for teaching and learning spread, Weiss said.

Feedback from the pilot, obtained through surveys and discussions with participants, will be shared among faculty and University leadership to advise Harvards strategy toward AI, according to Bailey.

The University has continued to negotiate with vendors on enterprise agreements that can help expand the variety of consumer AI tools available on the platform, per Bailey.

Weiss said he looks forward to future versions that include the ability to upload files like data and PDFs and the advanced data analysis tool in GPT-plus.

As generative AI has gained traction on campus and in the world, the Office of Undergraduate Education has rolled out guidance for faculty approaches regarding generative AI use in FAS courses. These guidelines range from maximally restrictive to fully-encouraging, though the FAS has not imposed a blanket policy on AI use.

Weiss said the pilot version of the AI sandbox has been received positively by his students.

Oh, this changes my whole job, Weiss said, quoting a conversation between two students in his class.

They really saw the magnitude of these tools in a way they hadnt. I think using them is a very important way to understanding them, Weiss added.

Staff writer Camilla J. Martinez can be reached at camilla.martinez@thecrimson.com. Follow her on X @camillajinm.

Staff writer Tiffani A. Mezitis can be reached at tiffani.mezitis@thecrimson.com.

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Harvard IT Launches Pilot of AI Sandbox to Enable Walled-Off Use ... - Harvard Crimson

Editors Note: A use case for generative AI is analyzing and synthesizing meeting notes. The rough notes I took during the session, "From Apprentice to Master: Artificial Intelligence and Policing," were submitted to ChatGPT 4.0 with the prompt: "Use these notes to create a news article of 500 to 700 words for police chiefs on the panel discussion." An additional prompt instructed ChatGPT to, "Add to this article, based on the notes, a checklist of immediate actions for a police chief to take to understand and prepare their agency for AI in policing."

SAN DIEGO In a recent panel discussion titled From Apprentice to Master: Artificial Intelligence and Policing, hosted by the International Association of Chiefs of Policein San Diego, California, law enforcement leaders from around the globe shared their insights on the emergence and integration of Artificial Intelligence (AI) in policing.

The eminent panel featured Donald Zoufal of the Illinois Association of Chiefs of Police, Shawna Coxon from An Garda Sochna, Dublin, Jonathan Lewin from INTERPOL Washington, Oscar Wijsman of the Netherlands Police Department, and Craig Allen, the chair of IACP Communications and Technology Committee.

One of the unequivocal conclusions from the discussion was that AI will be a game-changer for law enforcement, transforming traditional intelligence-led policing through a gamut of technological advancements. The copious amount of data now available, coupled with cloud technology and open source tools, lays the groundwork for leveraging machine learning, large language models (LLMs), and soon, quantum technology, to enhance various facets of policing.

AI's potential is vast, encompassing everything from street patrolling to criminal investigations and managerial functions. The technology will automate both simple and complex tasks, redefine the interaction between humans and machines, and enable law enforcement agencies to visualize phenomena and discern patterns critical for predictive policing and resource allocation. By revealing criminal networks and markets, understanding seized dataand monitoring the health of colleagues, AI is set to become an indispensable ally in the quest for justice.

In the Netherlands, where the National Police force numbers 65,000, over 100 personnel are already dedicated to the application of AI, organizing their efforts through a hub-and-spokes model. This model facilitates a common business process, robust AI and machine learning infrastructure, and a readiness in data management to accommodate new roles such as data analysts, ethicistsand digital forensic investigators, highlighting the broad impact of AI on organizational structures.

Despite the bright prospects, challenges loom large. The rapid pace of AI development is outstripping the relatively modest governmental regulatory efforts, creating a regulatory vacuum. Law enforcement agencies are encouraged to conduct a rigorous AI risk evaluation to understand, measure, and manage the risks to individuals, the organization, and the broader ecosystem. Key steps include mapping AI applications, tailoring contracting approachesand developing governance structures to ensure transparency, accountability and human review in AI implementations.

The panelists shared examples of how AI is already in use, highlighting its utility in facial and license plate recognition, fraud detection, text data analysisand victim support, to name a few. However, the flip side also came into focus as AI's potential for criminal misuse in fraud, scams, misinformation campaignsand cybercrime was discussed, underscoring the importance of a well-thought-out approach to AI integration in policing.

As the landscape of law enforcement technology enters this exciting new chapter, law enforcement leaders are urged to develop roadmaps for the near, mediumand long-term, build knowledge within their departments, and establish robust policies addressing the use, evaluationand human intervention in AI applications.

This illuminative session underlines a global acknowledgment of AI's potential to significantly elevate policing efforts, provided a balanced and well-regulated approach is adopted. It is a clarion call for law enforcement agencies to actively engage with, understandand integrate AI in a manner that augments their capabilities while safeguarding against the risks inherent in this powerful technology.

As the horizon of law enforcement broadens with the advent of Artificial Intelligence (AI), it's imperative for police chiefs to take decisive actions in acquainting and preparing their departments for this technological pivot. Here's a checklist of immediate actions that can set the groundwork for integrating AI in policing:

1. Definition and understanding of AI:

Publish a clear definition of what AI entails for your agency.

Foster an environment of learning and inquiry regarding AI and its potential impact on policing.

2. Current technology audit:

Conduct an audit to identify where AI or AI-capable technology is already in use within your department.

Evaluate the effectiveness and efficiency of current AI applications.

3. Develop a strategic AI roadmap:

Draft a near, medium, and long-term roadmap outlining the adoption and integration of AI in your departments operations.

Identify priority areas where AI could have the most significant positive impact.

4. Establish a governance structure:

Create a governance structure to oversee the ethical and responsible use of AI.

Designate roles and responsibilities for AI oversight, ensuring clear lines of accountability.

5. Policy development:

Develop and publish policies addressing the use of AI, including a statement of purpose, use policy, ongoing technology evaluation, human review, and intervention procedures.

Ensure policies are easily accessible and comprehensible to all members of the department.

6. Community engagement:

Engage with the community to explain how AI will be used in policing and to gather feedback.

Establish channels for ongoing community input and transparency regarding AI use.

7. Risk assessment:

Conduct a comprehensive risk assessment to understand the potential challenges and threats associated with AI.

Establish a mechanism for continuous risk evaluation and mitigation as AI technologies evolve.

8. AI procurement planning:

Understand the problems you aim to solve with AI and make a clear business case.

Tailor your contracting approach to ensure the chosen solutions meet your departments needs and adhere to established policies.

9. Training and education:

Implement training programs to build knowledge and skills necessary for leveraging AI.

Encourage cross-departmental education to ensure a unified approach to AI adoption.

10. Partnerships and collaborations:

Foster partnerships with other law enforcement agencies, governmental bodies, and academic institutions to stay abreast of AI advancements and best practices.

Explore collaborative opportunities for shared resources and knowledge exchange.

Taking these steps will not only prepare police chiefs and their departments for the integration of AI but will also build a strong foundation for navigating the challenges and maximizing the benefits of AI in advancing public safety.

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Advancing policing through AI: Insights from the global law ... - Police News

ALBANY (TNS) New Yorks state university system is poised to ramp up its focus on generative artificial intelligence and its impact, even as Gov. Kathy Hochul indicated that statewide regulation of the emerging technology is unlikely, especially in the near future.

In remarks delivered at the State University of New Yorks inaugural symposium on artificial intelligence on Monday at the University at Albany, Hochul highlighted an incoming $20 million collaboration between IBM and UAlbany that seeks to better position the states adoption of AI. Hochul sought to portray New York as the geographical nexus of research and job growth in the newly emerging industry, while acknowledging potential pitfalls.

Think about all the possibilities, the good and the bad, Hochul told conference attendees. And if theres bad lets anticipate, lets solve for it now before it becomes embedded in the systems.

But Hochul also said she believes states, including New York, do not have much of a role to play in regulating companies pushing generative AI, which proponents and opponents alike have said has the capacity to cause fundamental changes to entire industries. Hochul said the federal government should instead oversee any regulation of artificial intelligence.

Many have touted artificial intelligences ability to improve automation and some workplaces; others have pointed to potential loss of jobs and increasing risks associated with deep fake technology or other unsavory aspects of machine learning.

For individual states to piecemeal this off, it certainly makes it enormously complicated for all the companies to have 50 states, different regulatory schemes to deal with, Hochul told reporters. I will say this, Im very open-minded to innovations, ideas coming to us or areas where we can protect our citizens.

SUNY will also launch an AI research group tasked with developing the university systems approach to the technology across its dozens of research institutions. Hochul said that the conclusions researchers reach could shape what role governments, including New Yorks, should play in the technology.

SUNY Chancellor John B. King Jr. called the system-wide efforts to monitor and pioneer AI-related initiatives crucial to define the quality of our states future.

Here are your picks for the Best of the Capital Region 2023

We tallied the votes from this years Best of the Capital Region contest in 100 categories.

Artificial intelligence development and application into education and the workforce has exploded over the past year, with much of the popular attention focused on how we can assure ourselves that student work and even faculty work are original in the context of ChatGPT, King said. But we all understand the potential and the risks are so much bigger than that. AI is intruding upon, improving upon and impacting nearly every area of our lives.

ChatGPT is a language processing tool that uses AI technology to allow someone to have human-like conversations with a chatbot.

In recent years, New York has attempted to position itself as a hub for artificial intelligence and machine learning along with other technological research and development.

Hochul included $75 million last year for the University of Albany, an aggressive early adopter of the technology.

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Hochul announces new SUNY, IBM investments in AI - Olean Times Herald

Nvidia announced that its adding support for its TensorRT-LLM SDK to Windows and models like Stable Diffusion as it aims to make large language models (LLMs) and related tools run faster. TensorRT speeds up inference, the process of going through pretrained information and calculating probabilities to come up with a result like a newly generated Stable Diffusion image. With this software, Nvidia wants to play a bigger part on that side of generative AI.

TensorRT-LLM breaks down LLMs like Metas Llama 2 and other AI models like Stability AIs Stable Diffusion to let them run faster on Nvidias H100 GPUs. The company said that by running LLMs through TensorRT-LLM, this acceleration significantly improves the experience for more sophisticated LLM use like writing and coding assistants.

This way Nvidia can not only provide the GPUs that train and run LLMs but also provide the software that allows models to run and work faster so users dont seek other ways to make generative AI cost-efficient. The company said TensorRT-LLM will be available publicly to anyone who wants to use or integrate it and can access the SDK on its site.

Nvidia already has a near monopoly on the powerful chips that train LLMs like GPT-4 and to train and run one, you typically need a lot of GPUs. Demand has skyrocketed for its H100 GPUs; estimated prices have reached $40,000 per chip. The company announced a newer version of its GPU, the GH200, coming next year. No wonder Nvidias revenues increased to $13.5 billion in the second quarter.

But the world of generative AI moves fast, and new methods to run LLMs without needing a lot of expensive GPUs have come out. Companies like Microsoft and AMD announced theyll make their own chips to lessen the reliance on Nvidia.

And companies have set their sights on the inference side of AI development. AMD plans to buy software company Nod.ai to help LLMs specifically run on AMD chips, while companies like SambaNova already offer services that make it easier to run models as well.

Nvidia, for now, remains the hardware leader in generative AI, but it already looks like its angling for a future where people dont have to depend on buying huge numbers of its GPUs.

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Nvidia's banking on TensorRT to expand its generative AI dominance - The Verge

Artificial intelligence is now well established in MRFs as a tool for sorting material and dramatically reducing contamination. Now, multiple companies are taking AI to an earlier stage of the recycling process by mounting cameras on collection trucks.

The goal is to try to stop contamination at the source and improve worker safety, said Ken Tierney, product manager at AMCS Group, one of the companies offering AI technology for collection. AMCS recently announced that it has deployed its Vision AI solution for the first time on Peninsula Sanitary Services trucks in California.Our drive and goal is to automate as many of these processes as we can, Tierney told Resource Recycling. If we can reduce the load on the driver, theres a safety aspect there as well.

Meanwhile, Canada-based Prairie Robotics has been working with AI and collection vehicles for over five years. Sam Dietrich, CEO of Prairie Robotics, said the interest in using AI and automation in vehicles has been steadily increasing, making it an exciting time for the recycling industry.

Prairie Robotics started out in response to a Saskatchewan province RFP to use AI to monitor what was being dumped from collection trucks into landfills. Dietrich said after building that application for the province, the team realized that most people were not interested in the landfill data, because by then its too late. What people wanted was more data at the source, so we tried to dig into that more.

That led Prairie Robotics to install cameras on recycling and organics collection vehicles to identify contaminants at the individual household level.

What weve also done besides the data analysis and reporting side is build out a full education suite, Dietrich said. We can send personalized postcards, texts, emails, in-app notifications, to a resident and inform them of their specific sorting mistakes.

AMCS had a similar journey. Tierney said the company specializes in transportation operations, so it was aware of the problems MRFs faced with contamination.

He said AMCS started looking into the situation and leaned on its familiarity with cameras and sensor technology to develop a solution in partnership with the University of Limerick. It then worked with Peninsula Sanitary Service for about a year to pilot and further develop the technology.

Thats how we got to where we are today, he said. It was kind of a process of, Okay, we understand what the obstacles are. Are there any solutions out there at the moment that can meet that challenge? We discovered there was not and said, Look, how can we then tackle that problem?'

On Peninsula Sanitary Services trucks, AMCS equipped two cameras: one focused on the hopper and one to check whether bins are overfilled. The lift of the front loader triggers the cameras to record so AMCS can use GPS coordinates and other logistic information to connect bins to households.

Currently, six of Peninsula Sanitary Services trucks have been fitted with the cameras, with four more due to be fitted in the new year.

Extended producer responsibility legislation and other reporting requirements have helped drive the rise of AI in an industry that often lacks solid data.

Dietrich said working in British Columbia, where extended producer responsibility for various types of packaging has been in place for decades, helped Prairie Robotics learn a lot about how the data it collects can be used for EPR.

I think EPR is going to be a driving force, he said. And in terms of how we use AI to capture the data thats needed, I think were still in the early days, but its an exciting movement that were seeing.

He added that AI and automation also provide needed customer feedback to improve recycling.

Were the only industry that doesnt provide personal feedback to our users, he said. When you look at water, electricity, heating, you get monthly feedback in the form of a monthly bill. You know what your usage is.

Tierney said for AMCS, it was less about AI specifically and more about picking the right technology to solve the problem.

To automate data collection and analysis, AI is definitely that sweet spot, he said. It definitely fits in there.

Some of the legislative pressure is more indirect, Tierney said. For example, requirements to reduce the level of contamination, means you first need to measure the baseline and then track changes. Thats where the AI and automation systems come in.

As with any developing technology, there are still limitations that Tierney and Dietrich run up against.

Tierney said the first thing AMCS had to contend with was the challenging visual conditions in a hopper and building up the algorithm.

In a MRF, material is moving at a consistent speed, you can control the lighting conditions and its always the same, he said. Its easy to see the material. When youre on the collection vehicle, youre looking into a hopper. Every time you empty a container the picture looks different.

Dietrich also noted that to use AI in a vehicle, you need to not only identify the material, but track it as it moves, as well.

Very early on we realized items in a hopper can linger in a hopper for literally hours, it would seem, depending on the item, he said. We spent a lot of time in our early days recording videos and benchmarking.

However, as the technology becomes more widespread and refined, Dietrich is looking forward to being able to also use it to alert drivers if a hazardous waste item is put in a truck.

Its a popular customer request, he said, and something Prairie Robotics is still testing. The items can be taught to the AI easily enough, but Dietrich said the trick is then deciding what action happens.

What do you do with that data? he said. Were having conversations with customers on do you have to turn the truck off and stop if youre detecting a propane tank? This is not a situation for a postcard, its a situation for the driver.

Prairie Robotics is also training its systems to identify more kinds of contaminants and expanding its education platforms in partnership with its customers and how to use the data its collected for other things, such as increasing participation or better cart management.

Thats the direction we see ourselves going, he said. How do you use this data weve already captured to help us in other ways?

Tierney said soon, automation and AI will be the industry standard. Not only will that improve data collection, but it could attract a whole new generation of workers.

It makes the industry more attractive to the younger generations, he said. In the past, if you look at the waste and recycling industry it was not seen as the nicest or the most sought after industry to go into. But if you stand back and look at it now and look at the level of automation and the use of tools like AI and sensors and cameras systems that have been fitted not only on the vehicles but the facilities as well anyone interested in technology, thats really a growing area in the waste and recycling industry.

He also sees the approach of self-driving vehicles, which will make automated data collection even more necessary.

We need to develop these technologies now to have them ready, he said.

A version of this story appeared in Resource Recyclingon Oct. 16.

Original post:

AI expands from MRFs to vehicles - Plastics Recycling Update

A 21-year-old computer-science student has won a global contest to read the first text inside a carbonized scroll from the ancient Roman city of Herculaneum, which had been unreadable since a volcanic eruption inAD79 the same one that buried nearby Pompeii. The breakthrough could open up hundreds of texts from the only intact library to survive from Greco-Roman antiquity.

Luke Farritor, who is at the University of NebraskaLincoln, developed a machine-learning algorithm that has detected Greek letters on several lines of the rolled-up papyrus, including (porphyras), meaning purple.Farritor used subtle, small-scale differences in surface texture to train his neural network and highlight the ink.

When I saw the first image, I was shocked, says Federica Nicolardi, a papyrologist at the University of Naples in Italy and a member of the academic committee that reviewed Farritors findings. It was such a dream, she says. Now, I can actually see something from the inside of a scroll.

Hundreds of scrolls were buried by Mount Vesuvius in OctoberAD79, when the eruption left Herculaneum under 20 metres of volcanic ash. Early attempts to open the papyri created a mess of fragments, and scholars feared the remainder could never be unrolled or read. These are such crazy objects. Theyre all crumpled and crushed, says Nicolardi.

The Vesuvius Challenge offers a series of awards, leading to a main prize of US$700,000 for reading four or more passages from a rolled-up scroll. On 12 October, the organizers announced that Farritor has won the first letters prize of $40,000 for reading more than 10 characters in a 4-square-centimetre area of papyrus. Youssef Nader, a graduate student at the Free University of Berlin, is awarded $10,000 for coming second.

To finally see letters and words inside a scroll is extremely exciting,says Thea Sommerschield, a historian of ancient Greece and Rome at Ca Foscari University of Venice, Italy. The scrolls were discovered in the eighteenth century, when workmen came across the remains of a luxury villa that might have belonged to the family of Julius Caesars father-in-law. Deciphering the papyri, Sommerschield says, could revolutionize our knowledge of ancient history and literature.Most classical texts known today are the result of repeated copying by scribes over centuries. By contrast, the Herculaneum library contains works not known from any other sources, direct from the authors.

Until now, researchers were able to study only opened fragments. A few Latin works have been identified, but most of these contain Greek texts relating to the Epicurean school of philosophy. There are parts ofOn Nature, written by Epicurus himself, and works by a little-known philosopher named Philodemus on topics such as vices, music, rhetoric and death. It has been suggested that the library might once have been his working collection. But more than 600 scrolls most held in the National Library in Naples, with a handful in the United Kingdom and France remain intact and unopened. And more papyri could still be found on lower floors of the villa, which have yet to be excavated.

Brent Seales, a computer scientist who has helped set up the Vesuvius Challenge, and his team spent years developing methods to virtually unwrap the vanishingly thin layers using X-ray computed tomography (CT) scans, and to visualize them as a series of flat images. In 2016 Seales, who is at the University of Kentucky in Lexington, he reportedusing the technique to read a charred scroll from En-Gedi in Israel, revealing sections of the Book of Leviticus part of the Jewish Torah and the Christian Old Testament written in the third or fourth centuryAD. But the ink on the En-Gedi scroll contains metal, so it glows brightly on the CT scans. The ink on the older Herculaneum scrolls is carbon-based, essentially charcoal and water, with the same density in scans as the papyrus it sits on, so it doesnt show up at all.

Seales realized that even with no difference in brightness, CT scans might capture tiny differences in texture that can distinguish areas of papyrus coated with ink. To prove it, he trained an artificial neural network to read letters in X-ray images of opened Herculaneum fragments. Then, in 2019, he carried two intact scrolls from the Institut de France in Paris to the Diamond Light Source, a synchrotron X-ray facility near Oxford, UK, to scan them at the highest resolution yet (48 micrometres per 3D image element, or voxel).

Reading intact scrolls was still a huge task, however, so the team released all of its scans and code to the public and launched the Vesuvius Challenge. We all agreed we would rather get to the reading of whats inside sooner, than try to hoard everything, says Seales.

Around 1,500 teams were soon discussing and collaborating through the gamer chat platform Discord. The prizes were designed in phases, and as each milestone is reached, the winning code is released for everyone to build on. Farritor, who had always been interested in history and taught himself Latin as a child, got involved early on.

In parallel, Seales team worked on the virtual unwrapping, releasing images of the flattened pieces for the contestants to analyse. A key moment came in late June, when one competitor pointed out that on some images, ink was occasionally visible to the naked eye, as a subtle texture that was soon dubbed crackle.Farritor immediately focused on the crackle, looking for further hints of letters.

One evening in August, he was at a party when he received an alert that a fresh segment had been released, with particularly prominent crackle. Connecting through his phone, he ran his algorithm on the new image. Walking home an hour later, he pulled out his phone and saw five letters on the screen. I was jumping up and down, he says. Oh my goodness, this is actually going to work. From there, it took just days to refine the model and identify the ten letters required for the prize.

Papyrologists are excited, too. The word purple has not yet been read in the opened Herculaneum scrolls. Purple dye was highly sought-after in ancient Rome and was made from the glands of sea snails, so the term could refer to purple colour, robes, the rank of people who could afford the dye or even the molluscs. But more important than the individual word is reading anything at all, says Nicolardi. The advance gives us potentially the possibility to recover the text of a whole scroll,including the title and author, so that works can be identified and dated.

Yannis Assael, a staff research scientist at Google DeepMind in London, describes the Vesuvius Challenge as unique and inspirational.But it is part of a broader shift, he notes, in which artificial intelligence (AI) is increasingly aiding the study of ancient texts. Last year, for example, Assael and Sommerschieldreleased an AI tool called Ithaca, designed to help scholars glean the date and origins of unidentified ancient Greek inscriptions, and make suggestions for text to fill any gaps. It now receives hundreds of queries per week, and similar efforts are being applied to languages from Korean to Akkadian, which was used in ancient Mesopotamia.

Seales hopes machine learning will open up what he calls the invisible library.This refers to texts that are physically present, but no one can see, including parchment used in medieval book bindings; palimpsests, in which later writing obscures a layer beneath; and cartonnage, in which scraps of old papyrus were used to make ancient Egyptian mummy cases and masks.

For now, however, all eyes are on the Vesuvius Challenge. The deadline for the grand prize is 31 December, and Seales describes the mood as unbridled optimism.Farritor, for one, has already run his models on other segments of the scroll and is seeing many more characters appear.

This article is reproduced with permission and was first published on October 12,2023.

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AI Reads Ancient Scroll Charred by Mount Vesuvius in Tech First - Scientific American

When an algorithm-driven microscopy technique developed in 2021 (and able to run on a fraction of the images earlier techniques required) isnt fast enough, what do you do?

Dive DEEPer, and square it. At least, that was the solution used by Dushan Wadduwage, John Harvard Distinguished Science Fellow at the FAS Center for Advanced Imaging.

Scientists have worked for decades to image the depths of a living brain.They first tried fluorescence microscopy, a century-old technique that relies on fluorescent molecules and light.However, the wavelengths werent long enough and they scattered before they reached an appreciable distance.

The invention of two-photon microscopy in 1990 brought longer wavelengths of light shine onto the tissue, causing fluorescent molecules to absorb not one but two photons. The longer wavelengths used to excite the molecules scattered less and could penetrate farther.

But two-photon microscopy can typically only excite one point on the tissue at a time, which makes for a long process requiring many measurements. A faster way to image would be to illuminate multiple points at once using a wider field of view but this, too, had its drawbacks.

If you excite multiple points at the same time, then you cant resolve them, Wadduwage said. When it comes out, all the light is scattered, and you dont know where it comes from.

To overcome this difficulty, Wadduwages group began using a special type of microscopy, described in Science Advances in 2021. The team excited multiple points on the tissue in a wide-field mode, using different pre-encoded excitation patterns. This technique called De-scattering with Excitation Patterning, or DEEP works with the help of a computational algorithm.

The idea is that we use multiple excitation codes, or multiple patterns to excite, and we detect multiple images, Wadduwage said. We can then use the information about the excitation patterns and the detected images and computationally reconstruct a clean image.

The results are comparable in quality to images produced by point-scanning two-photon microscopy. Yet they can be produced with just hundreds of images, rather than to the hundreds of thousands typically needed for point-scanning. With the new technique, Wadduwages group was able to look as far as 300 microns deep into live mouse brains.

Still not good enough. Wadduwage wondered: Could DEEP produce a clear image with only tens of images?

In a recent paper published in Light: Science and Applications, he turned to machine learning to make the imaging technique even faster. He and his co-authors used AI to train a neural network-driven algorithm on multiple sets of images, eventually teaching it to reconstruct a perfectly resolved image with only 32 scattered images (rather than the 256 reported in their first paper). They named the new method DEEP-squared: Deep learning powered de-scattering with excitation patterning.

The team took images produced by typical two-photon point-scanning microscopy, providing what Wadduwage called the ground-truth. The DEEP microscope then used physics to make a computational model of the image formation process and put it to work simulating scattered input images. These trained their DEEP-squared AI model. Once AI produced reconstructed images that resembled Wadduwages ground-truth reference, the researchers used it to capture new images of blood vessels in a mouse brain.

It is like a step-by-step process, Wadduwage said. In the first paper we worked on the optics side and reached a good working state, and in the second paper we worked on the algorithm side and tried to push the boundary all the way and understand the limits. We now have a better understanding that this is probably the best we can do with the current data we acquire.

Still, Wadduwage has more ideas for boosting the capabilities of DEEP-squared, including improving instrument design to acquire data faster. He said DEEP-squared exemplifies cross-disciplinary cooperation, as will any future innovations on the technology.

Biologists who did the animal experiments, physicists who built the optics, and computer scientists who developed the algorithms all came together to build one solution, he said.

Originally posted here:

A DEEPer (squared) dive into AI Harvard Gazette - Harvard Gazette

Cognizant and Vianai will leverage conversational Large Language Model capabilities to help clients better explore their data, predict outcomes and unlock actionable insights

TEANECK, N.J. and PALO ALTO, Calif., Oct. 17, 2023 /PRNewswire/ -- Cognizant (Nasdaq: CTSH) and Vianai Systems, Inc.today announced the launch of a global strategic go-to-market partnership to accelerate human-centered generative AI offerings. This partnership leverages Vianai's hila Enterprise platform alongside Cognizant's NeuroAI, creating a seamless, unified interface to unlock predictive, AI-driven decision making. For both companies, this partnership is expected to enable growth opportunities in their respective customer bases, and through Cognizant's plans to resell Vianai solutions.

Vianai's hila Enterprise provides clients a platform to safely and reliably deploy any large language model (LLM), optimized and fine-tuned to speak to their systems of record both structured and unstructured data, enabling clients to better analyze, discover and explore their data leveraging the conversational power of generative AI.

"Being able to monitor and improve LLM performance is critical to unlocking the true power of gen AI," said Ravi Kumar S

In addition, the LLM monitoring capabilities within hila Enterprise (vianops) is a next-generation monitoring platform for AI-driven enterprises, which monitors and analyzes LLM performance to proactively uncover opportunities to continually improve the reliability and trustworthiness of LLMs for clients.

"In every business around the world, there is a hunger to harness the power of AI, but serious challenges around hallucinations, price-performance and lack of trust are holding enterprises back. That's why we built hila Enterprise, a platform that delivers trusted, human-centered applications of AI," said Dr. Vishal Sikka, Founder and Chief Executive Officer of Vianai Systems. "In Cognizant, we have found a strategic partner with a distinguished history of delivering innovative services. Together we will deliver transformative applications of AI that businesses can truly rely on, built on the trusted foundation of hila Enterprise and Cognizant's Neuro AI platform."

"Being able to monitor and improve LLM performance is critical to unlocking the true power of generative AI," said Ravi Kumar S, Cognizant's Chief Executive Officer. "With Vianai's platform and our Neuro AI platform, we believe we will be able to offer our clients a high-quality solution to support seamless data analysis with predictive decision-making capabilities."

Dr. Sikka announced this collaboration and partnership during Cognizant's Discovery Summit in Dana Point, California on Tuesday, October 17.

About CognizantCognizant (Nasdaq: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes and transform experiences so they can stay ahead in our fast-changing world. Together, we're improving everyday life. See how atwww.cognizant.comor @cognizant.

About Vianai Systems, Inc.

Vianai Systems, Inc.is a human-centered AI (H+AI) platform and products company focused on bringing trustworthy, responsible and transformative AI systems to enterprises worldwide. The company's hila Enterprise platform enables enterprises to safely and reliably deploy large language enterprise solutions, leveraging its Zero Hallucination technologies, veryLLM open-source code, breakthrough fine-tuning and optimization techniques as well as its LLM monitoring capabilities to analyze, discover and explore data within systems of record, in natural language. The hila Enterprise platform helps enterprises minimize the risks of AI, while taking full advantage of the transformation potential of reliable AI systems. A showcase of the underlying capabilities of hila Enterprise can be accessed for free by anyone to ask any financial question about publicly traded companies atwww.hila.ai.Follow @VianaiSystems onVianai TwitterandVianai LinkedIn.

SOURCE Cognizant Technology Solutions

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AI (Artificial Intelligence) letters are placed on computer motherboard in this illustration taken, June 23, 2023. REUTERS/Dado Ruvic Acquire Licensing Rights

Oct 16 (Reuters) - Florida lawyers might soon be required to get their client's consent before using artificial intelligence on their legal matters.

The Florida Bar is crafting a new advisory opinion focused on the use of AI and has asked Florida lawyers to weigh in. Florida bar leaders have tasked the Florida Board Review Committee on Professional Ethics with creating rules around the use of generative AI, such as OpenAI's ChatGPT, Google Bard or Microsoft's Bing.

In addition to a requirement that lawyers obtain their clients consent before using AI, the committee will also consider whether such AI is subject to the same lawyer supervision requirements as non-lawyer assistants, and whether lawyer fees should be lower when AI is used. It will also look at whether law firms should be allowed to advertise their generative AI as superior or unique, and whether lawyers may encourage clients to rely on due diligence reports generated by AI.

A number of federal judges have already said that lawyers must disclose when they have used artificial intelligence in matters that appear on their dockets. Those requirements came after a case in which two New York lawyers submitted a legal brief that included six fictitious case citations generated by ChatGPT. Lawyers at Levidow, Levidow & Oberman in June were ordered pay a $5,000 fine. They had said they were unaware that the technology could make up cases.

But Florida looks to be the first jurisdiction considering a consent rule for lawyers using AI. A raft of legal tech companies have unveiled new AI products in recent months that do everything from legal research to creating legal documents.

Florida bar members have until Dec. 1 to submit comments on the new AI advisory opinion. The committee drafting the opinion is slated to meet Nov. 30.

Read more:

New York lawyers sanctioned for using fake ChatGPT cases in legal brief

Another US judge says lawyers must disclose AI use

Our Standards: The Thomson Reuters Trust Principles.

Karen Sloan reports on law firms, law schools, and the business of law. Reach her at karen.sloan@thomsonreuters.com

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Florida bar weighs whether lawyers using AI need client consent - Reuters

Scientists are using AI to design synthetic proteins with hopes it will speed up the discovery process. Ian C Haydon/ UW Institute for Protein Design hide caption

Scientists are using AI to design synthetic proteins with hopes it will speed up the discovery process.

Artificial intelligence can code computer programs, draw pictures and even take notes for doctors. Now, researchers are excited about the possibility that AI speeds up the scientific process from quicker drug design to someday developing new hypotheses.

Science correspondent Geoff Brumfiel talks to host Regina Barber about his visit to one protein lab already seeing promising results.

Click here to read Geoff's full story.

Listen to Short Wave on Spotify, Apple Podcasts and Google Podcasts.

Have an AI query? Send us your questions to shortwave@npr.org.

This episode was produced by Berly McCoy and edited by Rebecca Ramirez. Geoff Brumfiel checked the facts and Maggie Luthar was the audio engineer.

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