Monthly Archives: February 2022

Foundation Fellow Nathan Safir is happiest when working on a complicated problem

As Nathan Safir was finishing up his masters in artificial intelligence at the University of Georgia, he had a big decision to make: Did he want to accept the Marshall Scholarship to pursue a Ph.D. in computer science in England or accept an offer to start work at Google this summer?

Safir, a Foundation Fellow, applied his problem-solving brain to the decision.

Mostly I tried to simplify it into a decision of Am I more excited to work now or am I more excited to go to grad school now? said Safir, who has a B.S. in computer science with a minor in geography.

Safir in the Institute for Artificial Intelligence in the Boyd Research and Education Center. (Photo by Chamberlain Smith/UGA)

Google was the winner. For now. Safir hopes to get his Ph.D. in artificial intelligence sometime in the future.

Whatever way he gets there, the end goal is the same he wants to work in artificial intelligence. Whether its working on a new cool application, existing AI methods, or actually doing the research, Id really love to work somewhere in that space, said Safir.

This flexibility will serve Safir well at Google, because he doesnt yet know what hell be doing at the company when he arrives in California this August. Theyll start team matching closer to my start date, said Safir. All I know is that Ill be in the Bay Area and possibly working on the Google Ads team.

During a Google internship in summer 2021, he worked with the Cloud AI team.

UGA has helped him find his passion for AI in several ways. One was a difficult math class his first year in college. I was a super eager freshman, so I took Math 3500. Its notorious for being very tough, and it was. I was glad I took it, but I realized I was less interested in the proof-based rigorous mathematics.

Nathan Safir outside of the Institute for Artificial Intelligence in the Boyd Research and Education Center. (Photo by Chamberlain Smith/UGA)

He decided to pick up machine learning instead. It presented an interesting way to use mathematical thinking and be able to think creatively.

Also helpful in guiding his path was UGAs Foundation Fellowship, a merit-based scholarship based in the Jere W. Morehead Honors College that is awarded to between 20-25 high school seniors out of 1,200 applicants. Its the reason Safir, who grew up in Kansas City, Missouri, ultimately chose UGA out of his ranked list of 13 potential schools.

He said he most appreciates the travel opportunities, although theyve been a bit truncated during the COVID years, and the people hes met along the way. The network of people you can talk to about careers or cool places you could work that type of thing was super helpful for me trying to imagine what I wanted to do in the future. Safir said he interacts with staff, peers and friends from the Fellowship on a daily basis.

Safirs gateway into programming was a documentary he saw in middle school on Mark Zuckerberg. It said he learned C++ from a book when he was about my age so I checked out the same C++ book from the library. I dont think I picked it up as quickly as he did, but that was the beginning of me trying to learn programming.

Ive been into math and quantitative problem solving since I was really young. Programming was just an extension of that logical problem-solving, he said.

Safir is a down-to-earth, well-rounded person who can move between playing in local Monday pick-up hockey games, coaching Athens Youth Hockey and working on his tennis game, to high-level problem-solving. But hes happiest when he has a tough question to work on.

My thesis is working on a theoretical problem of how to extend an architecture called variational autoencoders to work with both labeled and unlabeled data, he said. My lab, led by Dr. Quinn of the CS department, is at the intersection of biomedical imaging and artificial intelligence. Its a theoretical problem inspired by a real-world problem. Its an especially open fieldI feel like there are a million different things you can work on. Im just happy I can be a part of it.

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An interest in AI led this student to a job at Google - University of Georgia

From music streaming platforms to social media feeds and search engines, algorithms are used behind the scenes to tailor services to the unique preferences of individuals. Though the use of algorithms has been explored in health care since the origins of artificial intelligence, new strides in deep learning methods over the last decade are allowing clinicians to go after mass amounts of data that were previously inaccessible, transforming how doctors and clinical researchers detect, diagnose and treat disease.

In addition to higher data-computing capacities and advanced algorithms, clinicians can now input data through written and spoken words rather than only quantitative lab and imaging results. As they talk with patients about subjective feelings and pain levels, detailed interpretations can be coded to augment poking and prodding data collected through sensors, giving machine-learning algorithms a fuller picture. With enough input, algorithms will be able to output a series of patterns which physicians can then use in their clinical practice for better diagnoses and understandings of disease.

What has happened in the last eight or so years is that new results coming out of these deep learning methods are allowing us to go after hard data problems that werent accessible before, said Don Brown, the University of Virginias senior associate dean for research, Quantitative Foundation Distinguished Professor in Data Science and a professor in the Department of Engineering Systems and Environment.

Five years ago, Brown was approached by pediatric gastroenterologist Dr. Sana Syed, who was then working as a gastroenterology fellow in her first year on the UVA faculty. Syed was part of a team researching environmental enteropathy, a disease caused by long-term exposure to poor sanitation and hygiene that results in intestinal inflammation and is a major contributor to growth stunting in children living in developing countries around the world.

During her time as a fellow, Syeds role was essentially that of an algorithm. By sifting through thousands of images taken by a video capsule endoscopy a tiny wireless camera that works its way through a patients gut in six to eight hours Syed was tasked with tagging any abnormal images, such as polyps, bleeding and ulcers, to understand tissue structure and function in the context of inflammation. The identification of disease patterns would ideally minimize the need for doctors to conduct endoscopies (the collection of tissue from a patients intestine, which is not a sustainable practice in many low- and middle-income countries) to confirm diagnoses. The tedious task pushed Syed and her colleagues to explore the use of AI as a way of picking up patterns of disease in tissue samples, saving countless hours of analysis.

That is when Syed approached Don Brown in a pursuit to intersect big data and medicine. With such an accomplishment, physicians could begin to more quickly and more accurately predict important inflammatory bowel disease outcomes not only assisting physicians, but also giving patients more peace of mind.

What is going to happen is that a patient shows up, and their data is plugged into these larger algorithms that are trained to learn off patterns that may be representative of you. Then you will be able to predict a specific patients outcome, Syed said. The idea is that you will be able to get more tailored data and have the ability to give specific risk percentages.

In addition to identifying patterns in environmental enteropathy, Syed is researching the effectiveness of AI in other inflammatory bowel diseases, such as Crohns and celiac disease. According to Syed, algorithms that predict scar tissue development in Crohns patients, for example, or individualized risk percentages for thyroid disease and diabetes in celiac patients, would be game-changers in determining more accurate diagnosis and likelihood of disease, allowing clinicians to develop targeted medications and treatments.

That is the thinking we are moving toward: Lets not just try to stop the disease; we want to prevent it and cure it. That is the goal.

To ensure that AI in medicine is effective, Brown said that data scientists and clinical researchers must be aware of inherent biases prevalent in smaller data sets that are many times missed when that data is generalized.

Algorithms work in a way that gets it the highest reward, and if it can get that reward through a biased set of data, it will use it, Brown said. You have to make sure that you are giving [the algorithm] a fair cross section of data so that it can give you results that are truly answering the question that you are after.

For Syed, one of the biggest issues lies in the lack of publicly available data sets. When determining patterns in celiac patients, Syed and her team parsed through 2,000 to 3,000 electronic health records to collect data from the desired population. Instead of recreating the wheel by labeling hundreds of thousands of biopsies, Syed hopes to gather medical information through large industry partners such as Takeda, a R&D-driven global biopharmaceutical company.

A lot of this cross-thinking happens when there is open access to data, but that has to be cleaned and sorted and thoughtfully put out there, Syed said.

Once data is collected and confirmed to be bias-free, the final step is ensuring interpretability. When many clinicians do not have a background in data science, the only way to cross the disciplinary boundary is to ensure that the data is communicated effectively, which is a responsibility that falls back on the data science community.

At the heart of this is making it interpretable to the clinicians so that they get what it is that the system is telling them and how it can be used, and what worth it is, Brown said. And then, they can look at individual patients and decide what makes sense.

Machine learning algorithms are being used across medical disciplines throughout the world of health. Within UVA Grounds, algorithms are being used to predict the effects of cardiac disease treatments, understand health conditions through smart watches in UVAs Link Lab, and analyze real-time diabetes-monitoring data, to name a few. As Syed, Brown and other researchers continue to evolve their AI capabilities, clinicians will begin to transform the ways in which they can accurately predict, determine and treat disease.

The more I have learned about [big data], the more I have understood its potential impact in medicine across all specialties, Syed said.

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Medicine Meets Big Data: Clinicians Look to AI for Disease Prediction, Prevention - University of Virginia

Nonfungible tokens (NFTs) have been largely acquired as proof-of-profile pictures (PFPs) that represent a brand, embody culture or ultimately, reflect as a static status symbol. Blue-chip NFTs like the Bored Ape Yacht Club or Cool Cats were not originally backed by any tangible utility other than speculative value and hype, along with the promise of an illustrative roadmap, but in 2022, investors are looking for a little bit more.

However, nonfungible tokens are finding their use beyond branding and status symbols by attempting to build out an existence in the Metaverse and some are ambitious enough to start within it.

The Altered State Machine (ASM) Artificial Intelligence Football Association (AIFA) has introduced a novel concept to NFTs called nonfungible intelligence or NFI. By tokenizing artificial intelligence, the ASM AIFA has captured the attention of investors who are thinking long-term about the future of the Metaverse and decentralized play-to-earn (P2E) economies.

In fusing AI features to the three growing markets of gaming, decentralized finance (DeFi) and NFTs, the ASM AIFA has the potential to be a lucrative long-term bet.

As an investor, these are the strategies Ive considered when thinking about investing in the ASM AIFA, while also factoring in the impending tokenomics that will be integrated into the nascent blockchain P2E game.

The ASM AIFA genesis box collection is essentially a starter booster pack toward its ecosystem. A box includes four ASM AI agents or all-stars as well as an ASM brain, which is the intelligence that powers each ASM all-star.

Currently valued at 5.369 Ether (ETH) ($16,768.84), the box is a valuable bet for those who hold long-term convictions in the ASTO economy and its decentralized autonomous organization (DAO) but more so, the Metaverse as a whole.

Since the ASM AIFA intends to award its early adopters and players through its play-and-earn model, the genesis box is essentially equipped as a ASTO generating set-up.

According to the ASM AIFA whitepaper, each brain will be able to mine ASTO and each all-star will be able to generate ASTO through training. Not only is ASTO the utility token of the Altered State Machine metaverse, but it's also the governance token in the ASM ecosystem.

Furthermore, these brains are not only limited to the ASM AIFA collection. They will also be supported in other notable NFT projects like FLUFF World NFT, making it interoperable as well.

ASTO tokens are needed to train the AI all-stars and also to create more AI agents. AI agents do not have to be limited to playing soccer in-game, its ASM brain can be trained to be a trading bot as well since it's dependent on its learned memories.

The project launched on Oct. 18, 2021, and since hitting the secondary market, ASM AIFA genesis boxes have increased by nearly 1,200%, suggesting there is growing interest and owners have recognized the value of AI.

In the last seven days, the average sale price of the genesis boxes have been on a downward trend, dropping from 6.3 ETH to 5.3 ETH. It seems that even with a slight correction, the genesis boxes have not dipped below the 4.75 ETH in the last month.

Based on the price points of the items in the genesis box, the floor for the ASM all-stars currently costs 0.21 Ether ($654.37) with a team of four, totaling approximately $2,617.48. The cheapest ASM brain is currently priced at 3.92 Ether ($12,214.96) bringing the total sum of the contents in the box to approximately $14,832.44 or 4.77 Ether.

Essentially, at these current prices, buying a genesis box costs roughly the same as it would purchasing the items separately. However, both the ASM brains and all-stars have experienced price fluctuations that have previously made purchasing the box a cheaper alternative than buying the items separately.

Depending on an investor's motives and strategy, they could sort other methods to own a piece of the ASM metaverse.

ASM AIFA genesis brains are unique artificial intelligence-equipped NFTs and the architecture of the brain is currently under patent pending where owners will have full rights to the machine-learning (ML) model of their NFI.

This provides an added layer of utility toward the ASM economy and a unique feature is that the ASM brains do not always need a form (avatar) and can exist and function with the parameters of its trained memories.

The ASM brain is the most expensive piece of the collection that will also be able to mine ASTO tokens. In this way, an investor can potentially make back their original capital investment via the brain's token emission. Currently, the cheapest ASM brain is worth 3.92 Ether ($12,214.96,) a 300% increase in floor prices in the last 60 days.

The ASM brains retain their value largely in part because of their genome matrix whose attributes enable for the brain to be integrated in other worlds outside of the ASM ecosystem. Meaning, the brain can be used in other ecosystems.

According to the ASM roadmap, each ASM genesis brain is slated for an ASTO token drop. Investors who are looking for exposure with AI, could consider purchasing ASTO for a more financially feasible bet.

Theres no denying that the ASM AIFA project is not the cheapest entry to the ecosystem, but for those investors who are strongly interested in the developing features of NFIs, investors could consider investing into the token or the AI all-star agents.

ASTO is the native currency that will govern activity in the ASM ecosystem. Since its needed to train the ASM brain and any AI agent, there will be an economy of gym owners who will provide GPU cloud computing for every ML model. In return for their time and energy, gym owners will be rewarded in ASTO.

When the ASTO token launches, the ASM team will host an auction to determine the price of ASTO in a unique method they have dubbed discovery auction. ASTO will also be dropped to owners of the ASM all-stars and ASTO can be staked to mine the next generation of brains in the ecosystem. In preparation for AIFAs launch, the ASTO token could be desirable to load up on.

As NFTs are finding ways to justify their value outside of speculation and the ways they can be integrated in the Metaverse expands, projects are beginning to build from the inside out. Time will tell when we begin to see more projects beginning to integrate AI features, but ASM AIFA seems to be a top contender as one of the first movers.

The views and opinions expressed here are solely those of the author and do not necessarily reflect the views of Cointelegraph.com. Every investment and trading move involves risk, you should conduct your own research when making a decision.

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Heres why AI-equipped NFTs could be the real gateway to the Metaverse - Cointelegraph

Dearbell is a smart household welcome service and lighting fixture that employs Artificial Intelligence to register the arrival of each resident and deliver corresponding messages.

Its been said that with the arrival of each new generation, the more disconnected we become from one another, tightening our grip on our smartphones. Its no secret that the more technologically advanced we become as a society, the more comfortable we become withdrawing from IRL socialization to prioritize our online lives.

Designers: Juan Lee, Hyebin Lee, Banhu Jeong, and Daeun Yoo

Responding to this generational gap, designers look once more to technology for a solution. Dearbell, a smart household welcome service, is one of the more recent solution-based concepts from a team of designers based in Korea.

Following a research period of interviews and surveys, the design team found that most family members living under one roof feel disconnected from one another. Due to long work hours and technology overload, communication at home is sometimes perceived as nagging. In an effort to connect family members together through communication, Dearbell is conceptualized as a customizable, home greeting service for family members to get to know one another better.

Dearbell takes on the look of a light pendant that can either hang from your ceiling or be mounted to your wall. Projecting from the light fixture, holographic messages are created and delivered by family members to other family members. Depending on artificial intelligence for operation, visual haptic sensors register each household residents gestures, shoes, and smartphone to ensure accurate message delivery.

The team of designers goes on to explain, Dearbell projector is motivated from welcoming bell. Technically, it supports automotive focusing in multi-layers and AI projection mapping that shows the image on a certain object and recognizes gesture interaction.

Walking through the homes front door, residents take off their shoes and Dearbell alerts each individual family member of the total number of steps taken during the day as well as any messages left to be read. As the unread message is beamed from the light pendant, the message can be opened by its designated receiver once they put their hand over the message icon.

Once the message is read, the receiver can choose to leave a response in the form of emojis by shaking their fists, a Dearbell-detected hand gesture. In addition to curated messages, residents can ask Dearbell for some daily information as they prepare to leave the home, including, weather, public transportation, as well as a pre-made to-do list.

Wooden elements ground the stainless steel body with a touch of warmth and rustic charm.

Once home, Dearbell alerts residents of the steps taken throughout the day.

Through the Wood detail finishing at the bottom, it visually reveals the warm communication with the family, not the technical image.

Dearbell consists of soft shapes and textures that are not solid form, luxurious arch-shaped metal frame.

Dearbell can either be mounted to a vertical service or suspended from the ceiling.

Following a period of interviews and surveys, the team of designers recognized the generational gaps present in most households.

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This household greeting service uses AI to register resident's arrival and welcome them - Yanko Design

Recent strides in the efficacy of AI, the adoption of IoT devices and the power of edge computing have come together to unlock the power of edge AI.

This has opened new opportunities for edge AI that were previously unimaginable from helping radiologists identify pathologies in the hospital, to driving cars down the freeway, to helping us pollinate plants.

Countless analysts and businesses are talking about and implementing edge computing, which traces its origins to the 1990s, when content delivery networks were created to serve web and video content from edge servers deployed close to users.

Today, almost every business has job functions that can benefit from the adoption of edge AI. In fact, edge applications are driving the next wave of AI in ways that improve our lives at home, at work, in school and in transit.

Learn more about what edge AI is, its benefits and how it works, examples of edge AI use cases, and the relationship between edge computing and cloud computing.

Edge AI is the deployment of AI applications in devices throughout the physical world. Its called edge AI because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center.

Since the internet has global reach, the edge of the network can connote any location. It can be a retail store, factory, hospital or devices all around us, like traffic lights, autonomous machines and phones.

Organizations from every industry are looking to increase automation to improve processes, efficiency and safety.

To help them, computer programs need to recognize patterns and execute tasks repeatedly and safely. But the world is unstructured and the range of tasks that humans perform covers infinite circumstances that are impossible to fully describe in programs and rules.

Advances in edge AI have opened opportunities for machines and devices, wherever they may be, to operate with the intelligence of human cognition. AI-enabled smart applications learn to perform similar tasks under different circumstances, much like real life.

The efficacy of deploying AI models at the edge arises from three recent innovations.

Since AI algorithms are capable of understanding language, sights, sounds, smells, temperature, faces and other analog forms of unstructured information, theyre particularly useful in places occupied by end users with real-world problems. These AI applications would be impractical or even impossible to deploy in a centralized cloud or enterprise data center due to issues related to latency, bandwidth and privacy.

The benefits of edge AI include:

For machines to see, perform object detection, drive cars, understand speech, speak, walk or otherwise emulate human skills, they need to functionally replicate human intelligence.

AI employs a data structure called a deep neural network to replicate human cognition. These DNNs are trained to answer specific types of questions by being shown many examples of that type of question along with correct answers.

This training process, known as deep learning, often runs in a data center or the cloud due to the vast amount of data required to train an accurate model, and the need for data scientists to collaborate on configuring the model. After training, the model graduates to become an inference engine that can answer real-world questions.

In edge AI deployments, the inference engine runs on some kind of computer or device in far-flung locations such as factories, hospitals, cars, satellites and homes. When the AI stumbles on a problem, the troublesome data is commonly uploaded to the cloud for further training of the original AI model, which at some point replaces the inference engine at the edge. This feedback loop plays a significant role in boosting model performance; once edge AI models are deployed, they only get smarter and smarter.

AI is the most powerful technology force of our time. Were now at a time where AI is revolutionizing the worlds largest industries.

Across manufacturing, healthcare, financial services, transportation, energy and more, edge AI is driving new business outcomes in every sector, including:

AI applications can run in a data center like those in public clouds, or out in the field at the networks edge, near the user. Cloud computing and edge computing each offer benefits that can be combined when deploying edge AI.

The cloud offers benefits related to infrastructure cost, scalability, high utilization, resilience from server failure, and collaboration. Edge computing offers faster response times, lower bandwidth costs and resilience from network failure.

There are several ways in which cloud computing can support an edge AI deployment:

Learn more about the best practices for hybrid edge architectures.

Thanks to the commercial maturation of neural networks, proliferation of IoT devices, advances in parallel computation and 5G, there is now robust infrastructure for generalized machine learning. This is allowing enterprises to capitalize on the colossal opportunity to bring AI into their places of business and act upon real-time insights, all while decreasing costs and increasing privacy.

We are only in the early innings of edge AI, and still the possible applications seem endless.

Learn how your organization can deploy edge AI by checking out the top considerations for deploying AI at the edge.

Link:

What Is Edge AI and How Does It Work? - NVIDIA Blog

DUBLIN--(BUSINESS WIRE)--The "AI in Computer Vision Market by Technology, Solutions, Use Cases, Deployment Model and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

This report assesses the application of AI in computer vision systems used in conjunction with connected devices, hardware components, embedded software, AI platforms, and analytics. The report analyzes machine learning models and APIs used in computer vision systems along with the application of neural networks in AI analytics systems.

This research also evaluates the causal relationship of computer vision systems with IoT, Edge computing, and connected machines along with core hardware and software technology. The report also analyzes the relation of emotion AI with computer vision systems along with the market factors.

Select Report Findings:

Computer vision systems are dedicated to simulate the human visual system while analyzing the information extracted from photos and videos. They do this by way of mathematical operations in conjunction with signal processing systems to process both digital and analog images. These systems leverage both two dimensional and three-dimensional processes.

AI represents the ability to organize information and create outcomes in learning, decision-making, and problem-solving using a computer-enabled robotic system in the same way a human brain does. The integration of AI and computer vision systems enhance the accuracy of object identification, classification, and analysis of information.

Through leveraging AI, computer vision systems provide a robotic system in which vision sensing capabilities provide information about the environment. One of the best examples of this in practice is autonomous vehicles, which rely on computer vision and AI-based decision making for safe travel.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

2.1 Defining AI in Computer Vision

2.2 Artificial General Intelligence and Super Intelligence

2.3 AI and Computer Vision Market Predictions

2.4 AI Outcomes and Enterprise Benefits

2.5 Cognitive Computing and Swarm Intelligence

2.6 Market Driver and Opportunity Analysis

2.7 Market Challenge Analysis

2.8 Covid-19 Impact

2.9 Value Chain Analysis

2.10 Pricing Analysis

2.11 Hs Code 854231

2.12 AI Patent and Regulatory Framework

2.13 AI Public Policy Issues

3.0 Technology and Application Analysis

3.1 Technology Analysis

3.2 IoT Device Ecosystem: Consumer, Enterprise, Industrial, and Government

3.3 Machine Learning Model

3.4 Artificial Neural Networks

3.5 Emotion AI Analysis

3.6 Edge Computing and 5G Networks

3.7 Smart Machine and Virtual Twinning

3.8 Factory Automation and Industry 4.0

3.9 Building Automation and Smart Workplace

3.10 Cloud Robotics and Public Security

3.11 Predictive 3D Design

3.12 IoT Application and Big Data Analytics

3.13 AI Application Delivery Platforms

3.14 Enterprise Adoption and External Investment

3.15 Application and Industry Vertical Analysis

3.16 Use Case Analysis

4.0 Company Analysis

5.0 AI in Computer Vision Market Analysis and Forecasts 2022 - 2027

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/yo9y26

About ResearchAndMarkets.com

ResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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Global Market for AI in Computer Vision to Reach $73.7 Billion by 2027 - ResearchAndMarkets.com - Business Wire

Working to transform cognitive testing to help millions around the world, the Cogentivity team has developed a healthcare solution that spots early signs of cognitive impairments in individuals.Cognetivity was founded as Sina Habibi and Seyed-Mahdi Khaligh-Razavi bonded over how dementia had touched their lives while studying for PhDs at the University of Cambridge. The pair shared an overwhelming feeling that their loved ones had been diagnosed too late to reduce the diseases devastating toll. Recognising an issue in dementia diagnosis, they set up Cognetivity to fix it.

With deep sector experience in neuroscience, AI, medicine and neurology, AI Magazine speaks to Mazen Sobh from Cognetivity to learn how the company is delivering a new paradigm in global healthcare.

"Cognetivity Neurosciences is a company that was established in the UK and is a spinoff from Cambridge University. From the university, two scientists PhD students collaborated and developed an application that specialises in identifying early or subtle signs of cognitive impairments in individuals. This application, CognICA, which can be used on an iPad or smartphone, combines cutting-edge neuroscience with artificial intelligence. The technology which has been patented and also peer-reviewed, displays 100 images in quick succession, asking patients to simply identify whether they see an animal in each or not. Due to the fear or food response, humans react strongly to animal imagery, activating large areas of their brain. AI analysis compares their reactions to how others in their age group responded and this generates a result that indicates how healthy a patient's brain is when compared to normal, mildly impaired and severely impaired individuals.

"I am the Vice President for Commerical Development and Im based in Dubai. I'm responsible for the growth of the business in the Middle East with further plans to expand to the Far East and beyond."

"Our CognICA solution is a technology that empowers clinicians to make better decisions that can dramatically improve patient outcomes. We want the solution to be the de facto assessment tool for clinicians who want to look at cognitive behaviour and cognitive development for humans. The company has also developed a mobile app called OptiMind that can be downloaded on your iPhone. This app is a wellness app that objectively measures your everyday cognitive performance for personal use. OptiMind can highlight personal lifestyle correlations with your cognitive sharpness and provide scope for optimisation.

"Through this application, we provide a graphical view of the relationship between a users lifestyle and mental wellbeing. It allows users to assess what factors improve their cognitive performance and empowers them to alter their lifestyle choices to optimise their mental health."

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Cognetivity's AI solutions to transform cognitive testing - Healthcare Global - Healthcare News, Magazine and Website