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It's a storyline worthy of a crime drama. The victims' remains have been discovered, and now it's down to investigators to piece together who they were and where they came from. But the victims aren't people, they're endangered tigers.

The tiger population across Asia has suffered a massive decline over the last 100 years. There are now believed to be fewer than 4,500 wild tigers left in the world. The biggest culprit in the iconic animals' decline has been poaching to feed the illegal trade in tiger parts.

Some of those illegal products are used in traditional medicines, but tiger pelts are also highly prized as decorative items.

The unique stripy coats that make them so sought after, however, are also helping conservation workers tackle the poaching problem and they should soon get a big helping hand in those efforts from artificial intelligence (AI).

Campaigners have been hard at work building a database of photographs of individual tiger skins. The idea is to be able to identify and track where illegally traded pelts come from, so law enforcement agencies in different countries can be contacted and help shut down wildlife traffickers.

"Every tiger's stripe pattern profile is unique, just like our fingerprints" Debbie Banks, the Tigers & Wildlife Crime campaign leader at the London-based Environmental Investigation Agency (EIA), told French news agency AFP. "So, we can use tiger stripes when we see images of tigers that have been offered for sale online, or if we have images of tigers that have been offered for sale in marketplaces or seized. We can use those images to cross reference against images of captive tigers that might have been farmed for trade and we can help join the dots as to where do the tigers that we see in trade come from."

Poring over thousands of often crowd-sourced photographs of tiger skin rugs, carcasses and even stuffed tigers, Banks and her team at the EIA have been working to match the stripe patterns to individual tigers to track where the animals came from, but its painstakingly slow work.

That, they hope, is about to change.

The EIA was recently awarded one of the first grants from the Alan Turing Institute, a U.K.-based center for data, science and artificial intelligence named after the renowned WWII codebreaker, and it's now developing a tool that uses AI to do the meticulous work of comparison.

"We have a database of images of tigers that have been offered for sale or have been seized, and when new images emerge, when our investigators get new images, we need to scan those against the database. At the moment we are doing that manually, looking at the individual stripe patterns of each new image that we get and cross-referencing it against the ones we have in our database," explained Banks. "The idea is that the artificial intelligence will - basically the scientists will create an algorithm, which means that they identify the individual stripe patterns of a unique individual tiger."

The EIA has appealed to anyone who sees tigers, dead or alive, to submit photographs along with any identifying information available to help build out the database.

"To develop, train and test the technology we need thousands of images of individual tiger stripe patterns, sourced by EIA staff, other organizations and you!" the group said in a news release announcing the project.

"This is a unique opportunity for tiger lovers around the world to get hands-on and actually contribute directly to the future conservation of tigers," Banks said in a statement.

It's hoped the technology will one day be used to help other vulnerable species, and put the people exploiting those species out of business, but for now, the focus is on getting it up and running to help save the big cats.

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The AI of the tiger: A high-tech bid to save an iconic species - CBS News

Photo by University Relations

Khoa Luu and Han-Seok Seo

Could artificial intelligence be used to assist with the early detection of autism spectrum disorder? Thats a question researchers at the University of Arkansas are trying to answer. But theyre taking an unusual tack.

Han-Seok Seo, an associate professor with a joint appointment in food science and the UA System Division of Agriculture, and Khoa Luu, an assistant professor in computer science and computer engineering, will identify sensory cues from various foods in both neurotypical children and those known to be on the spectrum. Machine learning technology will then be used to analyze biometric data and behavioral responses to those smells and tastes as a way of detecting indicators of autism.

There are a number of behaviors associated with ASD, including difficulties with communication, social interaction or repetitive behaviors. People with ASD are also known to exhibit some abnormal eating behaviors, such as avoidance of some if not many foods, specific mealtime requirements and non-social eating. Food avoidance is particularly concerning, because it can lead to poor nutrition, including vitamin and mineral deficiencies. With that in mind, the duo intend to identify sensory cues from food items that trigger atypical perceptions or behaviors during ingestion. For instance, odors like peppermint, lemons and cloves are known to evoke stronger reactions from those with ASD than those without, possibly triggering increased levels of anger, surprise or disgust.

Seo is an expert in the areas of sensory science, behavioral neuroscience, biometric data and eating behavior. He is organizing and leading this project, including screening and identifying specific sensory cues that can differentiate autistic children from non-autistic children with respect to perception and behavior. Luu isan expert in artificial intelligence with specialties in biometric signal processing, machine learning, deep learning and computer vision. He will develop machine learning algorithms for detecting ASD in children based on unique patterns of perception and behavior in response to specific test-samples.

The duo are in the second year of a three-year, $150,000 grant from the Arkansas Biosciences Institute.

Their ultimate goalis to create an algorithm that exhibits equal or better performance in the early detection of autism in children when compared to traditional diagnostic methods, which require trained healthcare and psychological professionals doing evaluations, longer assessment durations, caregiver-submitted questionnaires and additional medical costs. Ideally, they will be able to validate a lower-cost mechanism to assist with the diagnosis of autism. While their system would not likely be the final word in a diagnosis, it could provide parents with an initial screening tool, ideally eliminating children who are not candidates for ASD while ensuring the most likely candidates pursue a more comprehensive screening process.

Seo said that he became interested in the possibility of using multi-sensory processing to evaluate ASD when two things happened: he began working with a graduate student, Asmita Singh, who had background in working with autistic students, and the birth of his daughter. Like many first-time parents, Seo paid close attention to his newborn baby, anxious that she be healthy. When he noticed she wouldnt make eye contact, he did what most nervous parents do: turned to the internet for an explanation. He learned that avoidance of eye contact was a known characteristic of ASD.

While his child did not end up having ASD, his curiosity was piqued, particularly about the role sensitivities to smell and taste play in ASD. Further conversations with Singh led him to believe fellow anxious parents might benefit from an early detection tool perhaps inexpensively alleviating concerns at the outset. Later conversations with Luu led the pair to believe that if machine learning, developed by his graduate student Xuan-Bac Nguyen, could be used to identify normal reactions to food, it could be taught to recognize atypical responses, as well.

Seo is seeking volunteers 5-14 years old to participate in the study. Both neurotypical children and children already diagnosed with ASD are needed for the study. Participants receive a $150 eGift card for participating and are encouraged to contact Seo athanseok@uark.edu.

About the University of Arkansas:As Arkansas' flagship institution, the UofA provides an internationally competitive education in more than 200 academic programs. Founded in 1871, the UofA contributes more than$2.2 billion to Arkansas economythrough the teaching of new knowledge and skills, entrepreneurship and job development, discovery through research and creative activity while also providing training for professional disciplines. The Carnegie Foundation classifies the UofA among the few U.S. colleges and universities with the highest level of research activity.U.S. News & World Reportranks the UofA among the top public universities in the nation. See how the UofA works to build a better world atArkansas Research News.

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Researchers Using Artificial Intelligence to Assist With Early Detection of Autism Spectrum Disorder - University of Arkansas Newswire

THE EXECUTIVE of an intelligence research lab has visions of programs intertwining with dreams.

The expectations for artificial intelligence in gaming are getting higher as chips and relevant technologies improve at a stunning pace.

1

Midjourney is an AI research lab that bills itself as "expanding the imaginative powers of the human species."

Their products demonstrate the many uses of AI, but CEO David Holz's vision is of programs that do more than turn words into images.

"You'll be able to buy a console with a giant AI chip and all the games will bedreams," Holz told PCGamer.

Easier said than done, but Midjourney's advisors include the CEO of the coding powerhouse Github and the creator of Second Life, one of the first encompassing virtual worlds.

"In theory, the barriers between consuming something and creating something fall away, and it becomes like liquid imagination flowing around the room," Holz continued.

Holz and Midjourney have a roadmap for taking these high-minded, philosophical applications of AI and making them real.

"Everything between now and then is a combination of increasing the quality, being able to do things like 3D, making things faster, making things higher resolution, and having smaller and smaller chips doing more and more stuff."

PCGamer's interview with Holz comes on the heels of a major breakthrough in computer chip development that could enable AI programs to be stored on locally instead of the cloud.

This would make wearables, like VR headsets used for gaming, better suited to run AI programs and taking a step toward AI doing more with a smaller footprint.

There have been flashes of terror over AI's power and Midjourney's own programs spat out a horrifying image when prompted to create the "last selfie ever taken".

Holz addressed the paranoia regarding AI and said "We're not trying to build God, we're trying to amplify the imaginative powers of the human species,"in his discussion with PCGamer.

Holz's dream-generated AI might seem like a long way off, but astechnologygets more advanced, society and industry are better equipped to improve technology faster and more drastically.

Brain-chip companies have begun human trials and Elon Musk tweeted that his brain-chip company Neuralink would have a "progress update show & tell" announcement on October 31

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Artificial intelligence will soon turn your dreams into video games, expert claims... - The US Sun

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence (AI) In Drug Discovery Global Market Opportunities And Strategies To 2031: COVID-19 Growth And Change" report has been added to ResearchAndMarkets.com's offering.

The global artificial intelligence (AI) in drug discovery market reached a value of nearly $791.8 million in 2021, having increased at a compound annual growth rate (CAGR) of 31.0% since 2016. The market is expected to grow from $791.8 million in 2021 to $2,994.5 million in 2026 at a rate of 30.5%. The market is then expected to grow at a CAGR of 25.4% from 2026 and reach $9,293.0 million in 2031.

Growth in the historic period in the artificial intelligence (AI) in drug discovery market resulted from growing adoption of artificial intelligence (AI) for cost efficient drug discovery, growing number of cross-industry collaborations and partnerships, and increasing use of artificial intelligence (AI) for tracking medication adherence. The market was restrained by shortage of skilled labor, challenges due to regulatory changes, low healthcare access, and high rate of AI project failures.

Going forward, rising adoption of cloud-based applications and services, increasing need to control drug discovery & development costs and reduce the overall time, and government initiatives in developing economies will drive market growth. Factors that could hinder the growth of the market in the future include incompatible legacy health IT infrastructure.

North America was the largest region in the artificial intelligence (AI) in drug discovery market, accounting for 44.0% of the total in 2021. It was followed by the Asia Pacific, Western Europe and then the other regions. Going forward, the fastest-growing regions in the artificial intelligence (AI) in drug discovery market will be South America and Asia Pacific where growth will be at CAGRs of 40.0% and 37.2% respectively during 2021-2026. These will be followed by Africa and Western Europe, where the markets are expected to register CAGRs of 34.4% and 33.2% respectively during 2021-2026.

The global artificial intelligence (AI) in drug discovery market is concentrated, characterized by the presence of global artificial intelligence (AI) in drug discovery providers. The top ten competitors in the market made up to 50.21% of the total market in 2020. Artificial intelligence (AI) has the potential to transform the pharmaceutical industry.

The top opportunities in the artificial intelligence (AI) in drug discovery market segmented by technology will arise in deep learning segment, which will gain $747.0 million of global annual sales by 2026. The top opportunities in the artificial intelligence (AI) in drug discovery market segmented by drug type will arise in small molecules segment, which will gain $1,287.0 million of global annual sales by 2026.

The top opportunities in the artificial intelligence (AI) in drug discovery market segmented by therapeutic type will arise in other diseases segment, which will gain $480.2 million of global annual sales by 2026. The top opportunities in the artificial intelligence (AI) in drug discovery market segmented by end-users will arise in pharmaceutical companies segment, which will gain $1,028.0 million of global annual sales by 2026. The artificial intelligence (AI) in drug discovery market size will gain the most in the USA at $621.3 million.

Scope

Markets Covered:

1) By Technology: Context-Aware Processing; Natural Language Processing; Querying Method; Deep Learning

2) By Drug Type: Small Molecule; Large Molecules

3) By Therapeutic Type: Metabolic Disease; Cardiovascular Disease; Oncology; Neurodegenerative Diseases; Respiratory Diseases; Anti-Infective Diseases; Other Therapeutic Areas

4) By End-Users: Pharmaceutical Companies; Biopharmaceutical Companies; Academic And Research Institutes; Others

Key Topics Covered:

1. Artificial Intelligence (AI) In Drug Discovery Market Executive Summary

2. Table of Contents

3. List of Figures

4. List of Tables

5. Report Structure

6. Introduction

7. Artificial Intelligence (AI) In Drug Discovery Market Characteristics

8. Artificial Intelligence (AI) In Drug Discovery Market Trends And Strategies

9. Impact Of COVID-19 On Artificial Intelligence (AI) In Drug Discovery

10. Global Artificial Intelligence (AI) In Drug Discovery Market Size And Growth

11. Global Artificial Intelligence (AI) In Drug Discovery Market Segmentation

12. Artificial Intelligence (AI) In Drug Discovery Market, Regional And Country Analysis

13. Asia-Pacific Artificial Intelligence (AI) In Drug Discovery Market

14. Western Europe Artificial Intelligence (AI) In Drug Discovery Market

15. Eastern Europe Artificial Intelligence (AI) In Drug Discovery Market

16. North America Artificial Intelligence (AI) In Drug Discovery Market

17. South America Artificial Intelligence (AI) In Drug Discovery Market

18. Middle East Artificial Intelligence (AI) In Drug Discovery Market

19. Africa Artificial Intelligence (AI) In Drug Discovery Market

20. Artificial Intelligence (AI) In Drug Discovery Market Competitive Landscape

21. Key Mergers And Acquisitions In The Artificial Intelligence (AI) In Drug Discovery Market

22. Artificial Intelligence (AI) In Drug Discovery Market Opportunities And Strategies

23. Artificial Intelligence (AI) In Drug Discovery Market, Conclusions And Recommendations

24. Appendix

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/rzodj8

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Artificial Intelligence (AI) In Drug Discovery Global Market Report 2022: Rising Adoption of Cloud-Based Applications and Services & Need to...

Alphabet and Amazon are among the companies best positioned to take advantage of future artificial intelligence disruption in the technology industry, our analysis shows.

The assessment comes from GlobalDatas Thematic Research ecosystem, which ranks companies on a scale of one to five based on their likelihood to tackle challenges like artificial intelligence and emerge as long-term winners of the technology sector.

According to our analysis, Alphabet, Amazon, Microsoft, Apple, Alibaba, Baidu, ByteDance, Nvidia, Z Holdings, Airbnb, Inspur Electronic, ASML, ABB, Tesla, Siemens, GE, Darktrace, Expedia, Mythic, Alibaba Pictures, Groq, Cerebras, TSMC, Horizon Robotics, UiPath, Automation Anywhere, SambaNova, iFlytek, AMD, Wayfair, Cambricon, Graphcore, Zapata Computing, Cambridge Quantum and Suning.com are the companies best positioned to benefit from investments in artificial intelligence, all of them recording scores of five out of five in GlobalDatas Advertising, Application software, Cloud services, Consumer electronics, Ecommerce, Enterprise security, Industrial automation, IT infrastructure, IT services, Music, Film, & TV, Publishing, Semiconductors, Social media and Telecom infrastructure Thematic Scorecards.

The table below shows how GlobalData analysts scored the biggest companies in the technology industry on their artificial intelligence performance, as well as the number of new artificial intelligence jobs, deals and patents from the companies since August 2021.

The final column in the table represents the overall score given to that company when it comes to their current artificial intelligence position relative to their peers. A score of five indicates that a company is a dominant player in this space, while companies that score less than three are vulnerable to being left behind. These can be read fairly straightforwardly.

The other data points in the table are more nuanced, showcasing recent artificial intelligence investment across a range of areas over the past year. These metrics, where available, give an indication of whether artificial intelligence is at the top of executives minds now, but high numbers in these fields are just as likely to represent desperate attempts to catch-up as they are genuine strength in artificial intelligence.

For example, a high number of deals could either indicate that a company is dominating the market, or that it is using mergers and acquisitions to fill in gaps in its offering.

This article is based on GlobalData research figures as of 19 August 2022. For more up-to-date figures, check the GlobalData website.

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Revealed: The technology companies leading the way in artificial intelligence - Verdict