Category Archives: Artificial Intelligence

JERSEY CITY, N.J., Aug. 30, 2022 /PRNewswire/ -- InsightAce Analytic Pvt. Ltd. announces the release of market assessment report on "Global Artificial Intelligence-powered (AI) Spatial Biology Market By Data Analyzed (DNA, RNA, and Protein) By Application (Translation Research, Drug Discovery and Development, Single Cell Analysis, Cell Biology, Clinical Diagnostics, and Other Applications) Technology Trends, Industry Competition Analysis, Revenue and Forecast Till 2030"

According to the latest research by InsightAce Analytic, the global artificial intelligence-powered (AI) spatial biology market is expected to record a promising CAGR of 16.4% during the period of 2022-2030. By region, North America dominates the global market with the major contribution in terms of revenue.

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In recent years, enormous advances in biological research and automated molecular biology have been gained using artificial intelligence (AI). AI has the ability to effectively assist in specific areas in biology, which may enable novel biotechnology-derived medicines to facilitate the deployment of precision medicine approaches. It is predicted that using AI on cell-by-cell maps of gene or protein activity will lead to major inventions in spatial biology. The next significant step in the comprehension of biology might be achieved by incorporating spatially resolved data. When applied to gene expression, spatial transcriptomics (spRNA-Seq) combines the strengths of conventional histopathology with those of single-cell gene expression profiling. Mapping specific disease pathologies is made possible by linking the spatial arrangement of molecules in cells and tissues with their gene expression state. Machine learning has the ability to generate images of gene transcripts at sub-cellular resolution and decipher molecular proximities from sequencing data.

Artificial Intelligence in spatial biology has gained faster development in sequencing and analysis, drug discovery, and disease diagnosis. Increased interest in AI in spatial biology can be attributed to the widespread use of similar technologies in other sectors and the growing popularity of increased use of Artificial Intelligence. Moreover, Market expansion can also be attributed to government spending on research around the world. The increasing demand for novel analysis analytical tools and subsequent funding has resulted in the market launch of high-throughput technology. However, Despite the availability of new high-complexity spatial imaging methods, it is still challenging and labour-intensive to extract, analyze, and interpret biological information from these images.

In 2021, the market was led by North America. Technological developments, the existence of a well-established research infrastructure and key players, and increased spending in drug discovery R&D are all factors contributing to the expansion of the regional market. Due to the region's large and growing demand from research and the pharmaceutical industry, North America is currently the largest market for artificial intelligence applications in spatial omics.

The major players operating in artificial intelligence-powered (AI) spatial biology market players areNucleai, Inc., Reveal Biosciences, Inc., Alpenglow Biosciences, SpIntellx, Inc., ONCOHOST, Pathr.ai, Phenomic AI, BioTuring Inc., Indica Labs, Rebus Biosystems, Inc., Genoskin, Algorithmic Biologics, Castle Biosciences, Inc. (TissueCypher), and Other Prominent Players. The leading spatial omics solution providers are focusing on strategies like investmenst for innovations, partnerships, collaborations, mergers, and agreements with AI based service providers.

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Key Developments In The Market

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Market Segments

Global Artificial Intelligence-powered (AI) Spatial Biology Market, by Data Analyzed, 2022-2030 (Value US$ Mn)

Global Artificial Intelligence-powered (AI) Spatial Biology Market, by Application, 2022-2030 (Value US$ Mn)

Global Artificial Intelligence-powered (AI) Spatial Biology Market, by Region, 2022-2030 (Value US$ Mn)

North America Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

Europe Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

Asia Pacific Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

Latin America Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

Middle East & Africa Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

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Other Related Reports Published by InsightAce Analytic:

Global Spatial Omics Solutions Market

Global Proteome Profiling Services Market

Global Single-Cell Bioinformatics Software and Services Market

Global Oligonucleotide Synthesis, Modification, and Purification Services Market

Global Circulating Cell-Free DNA (ccfDNA) Diagnostics Market

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Artificial Intelligence-powered (AI) Spatial Biology Market Market to Record an Exponential CAGR by 2030 - Exclusive Report by InsightAce Analytic -...

On August 9, 2022, President Biden signed the CHIPS Act of 2022 (the Act), legislation to fund domestic semiconductor manufacturing and boost federal scientific research and development (see our previous alert for additional background). As part of its science-backed provisions, the Act includes many of the U.S. Innovation and Competition Acts (USICA) original priorities, such as promoting standards and research and development in the field of artificial intelligence (AI) and supporting existing AI initiatives.

The Act directs the National Institute of Standards and Technology (NIST) Director to continue supporting the development of AI and data science and to carry out the National AI Initiative Act of 2020 (previous alert for additional background), which created a coordinated program across the federal government to accelerate AI research and application to support economic prosperity, national security, and advance AI leadership in the United States. The Director will further the goals of the National AI Initiative Act of 2020 by:

Furthermore, the Act provides that the Director may establish testbeds, including in virtual environments, in collaboration with other federal agencies, the private sector and colleges and universities, to support the development of robust and trustworthy AI and machine learning systems.

A new National Science Foundation (NSF) Directorate for Technology, Innovation and Partnerships (the Directorate) is established under the Act to address societal, national and geostrategic challenges for the betterment of all Americans through research and development, technology development and related solutions. Over the next five years, the new Directorate will receive $20 billion in funding. Moreover, the Directorate will focus on 10 key technology focus areas, including AI, machine learning, autonomy, related advances, robotics, automation, advanced manufacturing and quantum computing, among other areas.

Within the Department of Energy (DOE), the Act authorizes $11.2 billion for research, development and demonstration activities and to address energy-related supply chain activities in the ten key technology focus areas prioritized by the new NSF Directorate. Further, the Act authorizes $200 million for the DOEs Office of Environmental Management to conduct research, development and demonstration activities, including the fields of AI and information technology.

The Act directs NSF Director to submit to the relevant House and Senate congressional committees a report outlining the need, feasibility and plans for implementing a program for recruiting and training the next generation of AI professionals. The report will evaluate the feasibility of establishing a federal AI scholarship-for-service program to recruit and train the next generation of AI professionals.

The Akin Gump cross-practice AI team continues to actively monitor forthcoming congressional and administrative initiatives related to AI.

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Chips-Plus Artificial Intelligence in the CHIPS Act of 2022 - JD Supra

Ramji finds out how the aesthetics of our age are being revolutionized by the algorithmic influence of artificial intelligence

Look at this painting. Doesnt it look like an unknown work of Rembrandt? Would you believe it if I said that the painting was generated by an engine driven by artificial intelligence (AI)? And what if I say that painting was created just by carefully chosen words? Yes, words. Here is what I typed in:

Portrait of a beautiful young woman, magnificent palace, Rembrandt style lighting, hyper realistic, cinematic

They say a picture is worth a thousand words. Well, in this case, several words make up a picture. Yes, this is the new trend in AI which takes in your text inputs and generates beautiful images. Not only this, but it also gives you 4 options first and you can mix and match elements between them. You can even upscale any of the 4 options and get a bigger picture. Now look at this

Midjourney, an AI engine that lets you create such beautiful pictures with just words, is one of many platforms that are welcoming in the era of AI artistry.

So how do these AI engines work? Their algorithms work not in a set of instructions or rules, but learn to create a specific aesthetic by trawling over thousands of images and picking up elements what it thinks that matches with the set of words that you entered. Fascinating, isnt it?

The engine is trained to analyze the set of images that matches each word in the text prompt and then put together a combined image. Now that is remarkable. And soon, you could create any image with great accuracy.

It all started in 2009 when Google, in association with Mannheim University, developed an artificial neural network, an AI system that was modelled after the human brain. This computer vision program was aimed at identifying and enhancing patterns based on an existing set of data that has been fed to the system and processed. And many artists started using this to create abstract artwork using this system instead of traditional way of drawing or painting. In a way, Deep Dream paved the way for the other systems that we are talking about now.

According to an article published by Ahmed Elgammal, a professor of computer science and founder of the Art and Artificial Intelligence Laboratory at Rutgers University, these AI based engines use something called Generative Adversarial Networks (GANs) which was introduced by a scientist Ian Goodfellow in 2014.

As per this system, the algorithm has two neural networks as part of it. One is aptly called the Generator that generates random images and the other one is called Discriminator, which is taught through inputs fed by the developers. These inputs are nothing but a series of images (thousands of them) without any context that is fed into to algorithm so that it helps to learn each of these images and when it is time to generate its own image, it can judge what is best for the requirement. The input images are all fed without any label and let the algorithm decide what it wants to create.

There is more. Prof. Elgammals team at Art and Artificial Intelligence Laboratory has created something called Artificial Intelligence Creative Adversarial Network, AICAN in short. So, what does this do? It is an AI system that can create artwork on its own, with little or almost no human involvement. The artworks produced by this system are almost indistinguishable from those of human artists and have been exhibited worldwide. One such artwork was even sold for USD 16,000 (Rs 12,77,536) at an auction!

When I began to draft this article, I had heard only about Dalle E, another AI engine created by OpenAI that lets you create such images with text inputs. Look at the examples provided on their website.

But the problem was a long waiting list to test it. While I was reading more about it, I encountered something called Dall E mini created by Craiyon. This is not as accurate or detailed as Dall E but still gives you an idea of how these systems work.

Now as I started to learn more about such engines, I came across several more such AI engines called by various names, Stable Diffusion, Deep Dream, Dreamstudio and so on.

These engines all create artwork through artificial intelligence. However, all or most of them are experimental now and it does look promising how it will turn out in the immediate future. So go ahead and try any of these. Bring out the artist in you.

So, what does this mean for the future of art? These algorithms can produce new artwork as long as there are sufficient inputs to it. Someday, artists might use these algorithms to create original art or the algorithms themselves will create original art. Though this technology is still in its nascent stages, the possibilities are endless.

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Putting the 'Art' in Artificial Intelligence! Sify - Sify

The staff policies of companies around the world increasingly rely on technology to fill the workforce shortage, with almost 60% of them estimating an increase in the use of artificial intelligence (AI), robots or chatbots, while 37% foresee a more intensive collaboration with mobile app developers and providers over the next two years, according to the study Orchestrating Workforce Ecosystems, conducted by Deloitte and MIT Sloan Management Review.

Moreover, most companies consider it beneficial to organize their workforce as an ecosystem, defined as a structure relying on both internal and external collaborators, between whom multiple relationships of interdependence and complementarity are established, in order to generate added value for the organization.

Almost all the companies participating in the study (93%) claim that the so-called external employees, such as service providers, management consultants or communication agencies, fixed-term or project-based employees, including developers and technology solution providers, are already part of the organization. On the other hand, however, only 30% of companies are ready to manage a mixed structure of the workforce.

The main reasons behind the decision to turn to external labour resources are the desire to reduce costs (62%), the intention to migrate to an on-demand work model based on a variable staffing scheme (41%) or the need to attract more employees with basic skills (40%).

The results of the study indicate that the workforce can no longer be defined strictly in terms of permanent, full-time employees. The need for flexibility, increasingly evident lately, amid events that have disrupted the global economy, such as the COVID-19 pandemic or the war in Ukraine, has led companies to look for ways to add to the workforce other solutions, especially in markets where it is deficient. But employers who want to go further in this direction need to make sure that they comply with the labour laws applicable in their jurisdiction, which, from case to case, may be more permissive or more restrictive. In the particular case of Europe, attention and consideration to the new trends in the field of workforce orchestration within a company are still required as the legal framework has yet to catch up with the challenges such new practices bring, said Raluca Bontas, Partner, Global Employer Services, Deloitte Romania.

Almost half of the companies (49%) consider that the optimal staffing structure should include both internal and external collaborators, provided that the first category is dominant. At the same time, 74% of the surveyed directors believe that the effective management of external collaborators is essential for the success of their organization.

At the same time, 89% are convinced that it is important for the external workforce to be integrated into the internal one, in order to create high-performing teams. On the other hand, 83% consider that the two categories have different expectations that require distinct offers in terms of benefits, rewards or flexibility in the way of working.

The responsibility for the workforce strategy lies with the entire top management team, mainly with the CEO (45% of respondents) and the human resources director (41%), but also with the COO, the CFO, the strategy and the legal director, according to the study.

The Orchestrating Workforce Ecosystems study was conducted by Deloitte and the MIT Sloan Management Review among more than 4,000 respondents, executives working in 29 industries, from 129 countries across all continents.

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Companies increasingly rely on technology-based solutions such as artificial intelligence, robots or mobile applications to fill workforce shortage -...

ALBUQUERQUE, N.M., and GLASGOW, Scotland, Aug. 30, 2022 /PRNewswire/ -- Indica Labs, an industry leader in quantitative digital pathology and image management solutions, and The Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (iCAIRD), announced today an agreement to collaborate on the development of an AI-based digital pathology solution for the detection of cancer within lymph nodes from colorectal surgery cases. The primary aim of the innovative research project is to develop a tool which in the future may improve the efficiency of pathology teams within the National Health Service Greater Glasgow and Clyde (NHSGGC) reporting colorectal cancer cases and the detection of metastatic cancer in lymph nodes.

Funded by a combination of Innovate UK and industrial partners, and based in Scotland, and supported by the West of Scotland Innovation Hub, iCAIRD is one of the largest healthcare AI research portfolios in the UK. A collaboration of 30 partners from across the NHS, industry, academia and technology, the program is currently delivering 35 ground-breaking AI projects across radiology and pathology, having grown from just 10 projects at the outset in 2019. The mission of iCAIRD is to establish a world-class center of excellence for implementation of artificial intelligence in digital diagnostics.

Anonymized H&E slides from NHS Greater Glasgow and Clyde's digital pathology archive will be used to train, validate and test the algorithm, which is being developed collaboratively by iCAIRD and Indica Labs. The resulting algorithm will report negative and positive lymph node status and will be compared to pathologist reports. Furthermore, positively involved lymph nodes will be categorized into metastases, micro-metastases, and individual tumor cells.

Dr. Gareth Bryson, Consultant Pathologist at NHSGGC and Clinical Director for Laboratory Medicine of iCAIRD commented on the potential value this tool will bring to the NHS: "Our belief is that AI powered decision support tools, such as the one we are working on, may help to support pathologists by improving the process' efficiency, while simultaneously increasing sensitivity in detecting small metastasis which will direct patient therapy. Colorectal cancer resections are one of the most common cancer resection specimens and a disproportionate amount of pathologist's time is utilized in screening lymph nodes."

Indica Labs, based in Albuquerque, New Mexico, offers a suite of digital pathology image analysis solutions including HALO AI, and HALO AP; both of which will be utilized by Indica Labs and iCAIRD partners for the development of AI-based pathology solutions and their evaluation in an NHS digital pathology workflow.

HALO AI uses deep learning neural networks to classify and quantify clinically significant tissue patterns and cell populations. HALO AP is a CE-IVD certified software platform for digital anatomic pathology labs that can operate as a standalone case and image management system or can be fully integrated within LIS or HIS solutions. HALO AP supports a full range of tissue-based workflows, includingAI-assisted assays, quantitative analytics, synoptic reporting,tumor boards, and secondary consults. In addition to HALO AI and HALO AP, Indica Labs recently received a CE-IVD mark for HALO Prostate AI, a deep learning-based screening tool designed to assist pathologists in identifying and grading prostate cancer in core needle biopsies that is deployed using HALO AP.

"The team at Indica Labs is excited to collaborate with iCAIRD on the development and deployment of a state-of-the-art AI tool that aims to improve diagnostic accuracy, turnaround times, and laboratory efficiency for the benefit of both pathologists and colorectal cancer patients," commented Steven Hashagen, CEO Indica Labs.

HALO AP will be evaluated within simulated digital workflows at the pathology department in NHS GGC, using iCAIRD's research environment to demonstrate interoperability with clinical systems. HALO AP will be used as a platform to deliver the new colorectal cancer algorithm. Through this collaboration, diagnostic accuracy and efficiency will be compared between existing fully digital workflows and one that applies AI through HALO AP.

About Indica Labs

Indica Labs is the world's leading provider of computational pathology software and image analysis services. Our flagship HALO and HALO AI platform facilitates quantitative evaluation of digital pathology images. HALO Link facilitates research-focused image management and collaboration while HALO AP enables collaborative clinical case review. Through a combination of precision, performance, scalability, and usability our software solutions enable pharmaceutical companies, diagnostic labs, research organizations, and Indica's own contract pharma services team to advance tissue-based research, clinical trials, and diagnostics.

About iCAIRD

iCAIRD aims to bring clinicians, health planners and industry together, facilitating collaboration between research-active clinicians and innovative SMEs to better inform clinical questions, and ultimately to solve healthcare challenges more quickly and efficiently using AI. iCAIRD is funded by Innovate UK, under the UK Research and Innovation (UKRI) Industrial Strategy Challenge Fund (ISCF) "From Data to Early Diagnosis in Precision Medicine" challenge. For more information, visit https://icaird.com/ or email info@icaird.com.

Media Contact:

Kate Lillard TunstallIndica Labs, Inckate@indicalab.com

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Indica Labs Announces Collaboration with The Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (iCAIRD) for the...

Iterate Studios created a tool embedded in security cameras to help spot weapons and other questionable items on a school campus

NORTH CAROLINA, USA School safety is a top priority as students head back into the classroom. New technology is helping to provide an added layer of protection by spotting a potential threat before it's too late.

Iterate Studios created the technology more than a year ago mainly for commercial use. It works with existing security cameras paired with artificial intelligence to identify questionable items like weapons.

"It can spot guns, kevlar vests, knives," Iterate Studios CEO Jon Nordmark said. "It can even identify masks that look suspicious.

Once the threat is spotted, an alert is automatically shared. In the case of a school setting, it would then be up to each school or school district to establish the next steps and what safety protocols to follow.

It would be in the best interest of all the kids, the teachers to have a camera like that on a door that might be unprotected where a security guard cant be," Nordmark said.

WATCH THIS! New technology working to make schools safer.Tonight on WCNC Charlotte at 11pm we take a look at Iterate.ai and the use of artificial intelligence to help detect weapons and other suspicious items on school campuses before it's too late!

Iterate says the threat awareness technology is being used in 3,500 locations worldwide. For now, that does not include any school districts throughout the greater Charlotte area.

Iterate leaders are working to improve access and affordability by offering the tool for $1,000 a year per school.

We just provide the tool to spot the problem or the potential problem and then its up to the school to set the rule what happens after that," Nordmark said.

Contact Briana Harper atbharper@wcnc.comand follow her onFacebook,TwitterandInstagram.

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'Provide the tool to spot the problem' | Artificial intelligence technology working to improve school safety - WCNC.com

The latest episode of Everyday Law focused on Artificial Intelligence as evidence in court proceedings.

Host, Bob Clark, spoke to Judge Paul Grimm of the the United States District Court for the District of Maryland and Professor Maura Grossman of the Univeristy of Waterloo in Ontario, Canada, who together had previously authored an authoritative article in the Northwestern School of Law's Journal of Technology. entitled " Artificial Intelligence as Evidence".

Both Judge Grimm and Professor Grossman had taken somewhat atypical paths to their legal careers. Judge Grimm started his legal career in the military and Professor Grossman earned a PHD in psychology, actively practicing in that field for a number of years before she concluded the law was her future.

They are each now academics with Judge Grimm helming the Duke University Law School's Bolch Judcial Institute and Professor Grossman a research professor in the school of computer science at the University of Waterloo as well as an adjunct professor at Osgoode Hall Law School.

The origin of Professor Grossman and Judge Grimm's work together was in the context of issues arising in electronic discovery. Professor Grossman and her husband, Gordon Cormack have been instrumental in setting legal standards for dealing with e-discovery and Judge Grimm was at the forefont of judicial efforts to formulate rules regarding admissibility of such evidence.

The episode is an hour in length and after charting the fascinating career paths of the guests, turns to the fundamental question of what is artificial intelligence and what is problematic regarding its use in court.

Artificial intelligence is computers performing cognitive tasks. That these processes are often opaque is beyond dispute. The fundamental question for admissibility concerns what validation was done to ensure that the algorithims consistently and accurately produce their results.

Judge Grimm discussed the continuing evolution of evidentiary standards noting that blood spatter and hair fiber analysis as well as eyewitness identification have been increasingly subject to skeptical court scrutiny.

He indicated that judges need a set of tools and that approaches to A.I. have been derivative of Daubert and the changes it gave rise to in the Federal Rules of Evidence.

Considerations for admissibility include relevance, error rate and the prejudice associated with wrongful admission. Judge Grimm suggested that asking fundamental questions such as what the A.I. was designed to do, whether it has been peer reviewed.and whether its process can be explained, are important.

A recurrent stumbling block concerns " trade secrets" which is subject to a qualified privilege but may disqualify admisssion of an A.I. function, where the progenitor of the A.I. refuses to explain how it works for fear they will disclose the secret sauce which distinguishes its product from a competitors.

As with all evolving evidentiary issues it is likely that the usefullness of the technology must be balanced against the prejudice its use entails and the party adversely affected must be afforded the opportunity to explore the possibility that its output is inaccurate.

For more go to:https://everydaylaw.podbean.com/e/artificial-intelligence/

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Artificial Intelligence as Evidence on Everyday Law - Maryland-Law.com

The dark regions of craters and mountainous terrain near the Moons south pole are key targets for future lunar missions like Artemis III. According to NASA, these dark sites have the potential to harbour coveted water ice, which could be broken down into its oxygen and hydrogen components in order to provide both life-sustaining air and potential fuel. This is because the shadowed regions are incredibly cold with temperatures as low as -274 to -400 F which traps the ice by stopping it from sublimating into a gas.

In their study, glaciologist Dr Valentin Bickel of ETH Zrich and his colleagues worked with images taken by NASAs Lunar Reconnaissance Orbiter, which has been documenting the surface of the Moon for more than a decade.

The spacecrafts camera, the team explained, captures photons particles of light that are bounced into the shadowed regions of the lunar surface from adjacent mountains and crater walls.

With the help of AI, the team have been able to make such efficient use of the data captured by the orbiter that even the darkest regions of the Moon have become visible.

Crucially, their analysis has revealed that no water ice is visible on the surface of the Moons shadowed areas even though such has been detected in these regions by other instruments.

Dr Bickel said: There is no evidence of pure surface ice within the shadowed areas.

This, he added, implies that any ice must be mixed with lunar soil or lie underneath the surface.

The new study is part of a larger investigation of potential landing sites and exploration options on the lunar surface being conducted by the Lunar and Planetary Institute (LPI) and the Johnson Space Center (JSC)s Center for Lunar Science and Exploration.

To date, the researchers said, they have examined more than half-a-dozen potential landing sites on the Moon.

READ MORE: NASA to venture into 'dark, unexplored' regions of Moon

Looking further into the future, the team explained, the findings will help NASA precisely map out safe routes into and through the Moons permanently shadowed regions for the Artemis programme.

This will greatly reduce the risk of misfortune for astronauts and robotic explorers traversing the lunar surface in the future.

Furthermore, the new images of the Moon will help target specific locations for sample collection to best assess the distribution of water ice on the Moon.

The full findings of the study were published in the journal Geophysical Research Letters.

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Artemis: Artificial intelligence shines a light on the Moons permanently shadowed regions - Express

Optometry Times' Alex Delaney-Gesing speaks with Leo P. Semes, OD, FAAO, professor emeritus of optometry at the University of Alabama-Birmingham, on the highlights and key takeaways from his discussion titled "Artificial intelligence and telemedicine," presented during the 115th annual South Carolina Optometric Physicians Association (SCOPA) meeting in Hilton Head, South Carolina.

Editor's note: this transcript has been lightly edited for clarity.

Could you share a highlights version of your presentation?

Artificial Intelligence (AI) is a topic that I've been following for probably 5 or so years. And as I dug into the history, it's quite interesting; it really began back in the 1930s. So it has quite a long history. It's based on algorithms and whether that algorithm is something as simple as how you do addition of big numbers or long division

The algorithm for looking at, for example, a patient with diabetic retinopathy, is specifying the severity of that, and then using that as a determination for treatment. And then if the patient is treated, following that patient to see if there is stagnation, stability of the diabetic retinopathy, or regression, which is what we're hoping for.

And some of the AI paradigms now demonstrate that there is the possibility of regression of diabetic retinopathy, from a physical standpoint, of how the retina looks, and also in terms of visual performance. And that's what to me is probably the most exciting aspect of what we can do with AI; to say, Okay, this is a patient who's got a certain level of diabetic retinopathy, the patient qualifies for treatment. Then 3 months following treatment, yes, the retina looks better, but they have improvement in visual performance.

So visual acuityquantitativelynumbers look better. And as a consequence of that, patients could enjoy a better lifestyle.

Why would you say this is such an important topic of discussion? Well, one of the reasons is thataside from age-related macular degeneration (AMD) one of the major causes of vision loss, especially among the working age population. is secondary to diabetic retinopathy (DR). And it's estimated that there's a segment of the population perhaps as high as 25%, who have pre-diabetes. So patients presenting for a vision exam, or vision irregularities, or even a periodic examination, might be discovered with certain changes that relate to DR. And then a diagnosis is made and the patient can be managed systemically, as well as ocularly.

What are the key takeaways you'd like attendees to learn from this?Probably the biggest thing is going to be the new staging paradigms for DR and how those relate to when a patient is going to need treatment. And if the patient is not at high risk and not a candidate for treatment, then emphasizing to the patient the importance of maintenance of systemic management strategies, and regular ophthalmic exams.

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SCOPA: Intersection of artificial intelligence and telemedicine - Optometry Times

From Robocop to Minority Report, the intersection between policing and artificial intelligence has long captured attention in the realm of high-concept science fiction. However, only over the past decade or so has academic research and government policy begun to focus on it.

Teagan Westendorfs ASPI report, Artificial intelligence and policing in Australia, is one recent example. Westendorf argues that Australian government policy and regulatory frameworks dont sufficiently capture the current limitations of AI technology, and that these limitations may compromise [the] principles of ethical, safe and explainable AI in the context of policing.

My aim in this article is to expand on Westendorfs analysis of the potential challenges in policings use of AI and offer some solutions.

Westendorf focuses primarily on a particular kind of policing use of AI, namely, statistical inferencing used to make (or inform) decisionsin other words, technology that falls broadly into the category of predictive policing.

While predictive policing applications pose the thorniest ethical and legal questions and therefore warrant serious consideration, its important to also highlight other applications of AI in policing. For example, AI can assist investigations by expediating the transcription of interviews and analysis of CCTV footage. Image-recognition algorithms can also help detect and process child-exploitation material, helping to limit human exposure. Drawing attention to these applications can help prevent the conversation from becoming too focused on a small but controversial set of uses. Such a focus could risk poisoning the well for the application of AI technology to the sometimes dull and difficult (but equally important) areas of day-to-day police work.

That said, Westendorfs main concerns are well reasoned and worth discussing. They can be summarised as being the problem of bias and the problem of transparency (and its corollary, explainability).

Like all humans, police officers can have both conscious and unconscious biases that may influence decision-making and policing outcomes. Predictive policing algorithms often need to be trained on datasets capturing those outcomes. Yet, if algorithms are trained on historical datasets that include the results of biased decision-making, it can result in unintentional replication (and in some cases amplification) of the original biases. Efforts to ensure systems are free of bias can also be hampered by tech-washing, where AI outputs are portrayed (and perceived) as based solely on science and mathematics and therefore inherently free of bias.

Related to these concerns is the problem of transparency and explainability. Some AI systems lack transparency because their algorithms are closed-source proprietary software. But it can be difficult to render even open-source algorithms explainableparticularly those used in machine learningdue to their complexity. After all, a key benefit of AI lies in its ability to analyse large datasets and detect relationships that are too subtle for the human mind to identify. Making models more comprehensible by simplifying them may require trade-offs in sensitivity, and therefore also in accuracy. Together these concerns are often referred to as the AI black box (inputs and outputs are known, but not what goes on in the middle).

In short, a lack of transparency and explainability makes the detection of bias and discriminatory outputs more difficult. This is both an ethical concern and a legal one when justice systems require that charging decisions be understood by all parties to avoid discriminatory practices. Indeed, research suggests that when individuals trust the process of decision-making, they are more likely to trust the outcomes in justice settings, even if those outcomes are unfavourable. Explainability and transparency can therefore be important considerations when seeking to enhance public accountability and trust in these systems.

As Westendorf points out, steps can be taken to mitigate bias, such as pre-emptively coding against foreseeable biases and involving human analysts in the processes of building and leveraging AI systems. With these sorts of safeguards in place (as well as deployment reviews and evaluations), use of AI may have the upshot of establishing built-in objectivity for policing decisions by reducing reliance on heuristics and other subjective decision-making practices. Over time, AI use may assist in debiasing policing outcomes.

While theres no silver bullet for enhancing explainability, there are plenty of suggestions, particularly when it comes to developing AI solutions to enhance AI explainability. Transparency challenges generated by proprietary systems can also be alleviated when AI systems are owned by police and designed in house.

Yet the need for explainability is only one consideration for enhancing accountability and public trust in the use of AI systems by police, particularly when it comes to predictive policing. Recent research has found that peoples level of trust in the police (which is relatively high in Australia) correlates with their level of acceptance of changes in the tools and technology used by police. In another study, participants exposed to purportedly successful policing applications of AI technology were more likely to support wider police use of such technologies than those exposed to unsuccessful uses, or not exposed to examples of AI application at all. In fact, participants exposed to purportedly successful applications even judged the decision-making process involved to be trustworthy.

This suggests that focusing on broader public trust in policing will be vital in sustaining public trust and confidence in the use of AI in policing, regardless of the degree of algorithmic transparency and explainability. The goal of transparent and explainable AI shouldnt neglect this broader context.

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Artificial intelligence and policing: it's a matter of trust | The Strategist - The Strategist