Daily Archives: May 28, 2022

Auterion, the company building an open and software-defined future for enterprise drone fleets, today announced the availability ofAI Nodefor pre-order, an onboard computer for drones and other mobile robots that adds supercomputer performance to Skynode, right at the edge. AI Node easily integrates withAuterions open ecosystem, resulting in a greater choice of powerful solutions for end users.

AI Node is equipped with the NVIDIA Jetson Xavier NX, the worlds smallest AI supercomputer for embedded and edge systems, which enables the direct processing of high bandwidth sensor data for better decision-making during operations. Compute-heavy AI and ML algorithms for object recognition, tracking and counting can be used, on mission, in advanced applications for public safety, security, and wildlife conservation, and across industry use cases.

As enterprises leverage more powerful cameras and sensors on drones,, the huge amount of data being created will overload any current data link, including 5G, said Markus Achtelik, vice president of engineering atAuterion. Its much more efficient to process raw data onboard via supercomputer, so that the operator or even the software itself can engage in real-time decision-making. AI Node delivers the horsepower to run modern neural networks in parallel and distill data from multiple high-resolution sensorswhich translates into faster innovation for enterprises and other organizations.

With AI Node, drone manufacturers, like Watts Innovations, can now easily build systems capable of running high performance AI algorithms onboard.

Some of our customers have very specific requirements necessitating a considerable amount of onboard computing, said Bobby Watts, CEO and principal engineer at Watts Innovations. For this, we turned toAuterionand AI Node, which allows us to run GPU intensive software onboard for applications such as vision-based precision landing, real-time mission navigation and other vision-based capabilities. Because AI Node is a part ofAuterions tight ecosystem, the integration and implementation is as clean as could be.

Other benefits of AI Node include:

Software developers, like Spleenlab, are enabling new and more advanced solutions for end customers. With AI Node, drone manufacturers and integrators can deploy Spleenlabs compute-heavy VISIONAIRY software and advanced AI algorithms directly onboard drones, said Stefan Milz, CEO of Spleenlab. This allows them to increase the safety of systems by executing tasks like ground-risk estimations. Working withAuterions AI Node is a win-win for us as providers and users of their software. We especially appreciate how easy it is to connect and collaborate with other manufacturers within their ecosystem.

Get in touchfor a demo and early access to AI Node for Skynode.

AboutAuterion

Auterionis building the worlds leading autonomous mobility platform for enterprise and government users to better capture data, carry out high-risk work remotely, and deliver goods with drones.Auterions open-source-based platform was nominated by the U.S. government as the standard for its future drone program. With 70+ employees across offices in California, Switzerland, and Germany,Auterions global customer base includes GE Aviation, Quantum-Systems, Freefly Systems, Avy, Watts Innovations, and the U.S. government.

Learn more aboutAuterionathttps://auterion.com/.

Originally posted here:

Auterion Delivers Supercomputer Performance Onboard Drones and Mobile Robots With AI Node - sUAS News

This is a five-part blog series from an interview that I recently had with Grace Lee, Chief Data and Analytics Officer and Dr. Yannick Lallement, Vice President, AI & ML Solutions at Scotiabank.

Scotiabank is a Canadian multinational banking and financial services company headquartered in Toronto, Ontario. One of Canada's Big Five banks, it is the third largest Canadian bank by deposits and market capitalization. With over 90,000 employees globally, and assets of approximately $1.3 trillion Scotiabank has invested heavily in AI, Analytics and Data and aligned an integrated function that is well supported by all business lines. Although their journey has zig zagged in impact along its way, the organization now has a strong foothold in bringing consistent value and impact to the business. We can all learn a great deal from these words of wisdom in what it takes to advance AI successfully in a large enterprise.

This five-part blog series answers these five questions:

Blog One: How is the advanced analytics function structured and what have been some of the most significant operational challenges in your journey?

Blog Two: What does it take to set up an AI/ML Solutioning Competency Center?

Blog Three: How are some of the operational challenges like Digital Literacy impacting your journey?

Blog Four: What are some of the operational lessons learned?

Blog Five: What does the future hold for Scotiabanks Advanced Analytics and AI function?

Advanced Analytics and AI at Scotiabank

How is your organization structured in terms of analytics, data and AI?

If you've been following our recent history, you would know that we've had a lot of fits and starts. We've had some aborted attempts to bring analytics and data to the Bank in a meaningful way. And through this journey, we have learned from our mistakes to enable us to move from siloed analytics, data, and AI professionals into a unified centre of excellence where we have integrated teams across the various business lines and functions. Prior, we had data in a primarily governance function in our risk management function where they were primarily focused on data quality but did not do much data enablement or delivery.

We currently have over 500 analytics, data, and AI professionals, and about half are skilled in AI. We have quite a diverse team in terms of skills, ranging from business analysts, user-centric designers, data scientists, data engineers, NLP specialists, ModelOps engineers, as well as resources skilled in data and AI ethics. Our people are primarily in North America (75%) and the balance of our talent is located in different global regions, in Mexico, South America (Peru, Chile, Colombia), the Caribbean, etc.

We are proud that our team consists of people that can ensure that our AI modelling and ML solutions are designed and deployed effectively from inception to consumption (Verbatim: Grace Lee).

What were some of the most significant lessons learned in your organizational restructuring journey?

Simply having AI, analytics, and data as capabilities does not mean that we are driving value, and if we don't drive value, we don't have a place in the Bank. So, one of the things we said we must do differently is, rather than put the function in technology, in operations, or in marketing where these teams often live, we will have data and analytics report directly to the business lines. We had to ensure that the value was from the business users using the solutions and driving tangible value (Verbatim: Grace Lee).

What were some of the technical lessons learned?

We learned that by bringing data and analytics tightly together, aligned with technology, and by having priorities and shared goals set by the business, it's less about the sophistication of the model and it's more about the meaningfulness of the outcome.

We have learned that we must work together closely in this ecosystem that we've built. This allows us to activate the virtuous cycle between data, analytics, and technology because technology is necessary to make data; data is necessary to make models; and models must be reintegrated into technology in order to get in front of a customer and employee by being embedded into the operating process. If we don't ensure process integration, we are not working in harmony.

For example, if we built an AI model where data pipelines are built one-off and not sustainable, when something changes in the technology, the models will stop properly functioning and supporting the business this scenario is antithetical to the way that we think about delivering value. When we talk about bringing data and analytics together, it's not just data governance, it's data delivery. Our concept of a reusable authoritative data set underpinning models to ensure operational sustainability is factored in from the onset and is core to our strategy.

This allows us to provide an abstraction layer that allows the end-user data to remain consistent and persist - so if something changes in the systems upstream, we are still able to deliver that same high quality data to all of our models. This means our reports and our processes are, in a way, relatively insulated from technology change. In other words, as you know in AI, often 80% of the problem is in the data sourcing; with well-managed and accessible data, we expect it to be closer to 20%. (Verbatim: Dr. Yannick Lallement)

Note: See Blog Two: What does it take to set up an AI/ML Solutioning Competency Center?

See more here:

Scotiabank Is Leading The Way With Advanced Analytics And AI - Forbes

AI is transforming engineering in nearly every industry and application area. With that, comes requirements for highly accurate AI models. Indeed, AI models can often be more accurate as they replace traditional methods, yet this can sometimes come at a price: how is this complex AI model making decisions, and how can we, as engineers, verify the results are working as expected?

Enter explainable AI a set of tools and techniques that help us to understand model decisions and uncover problems with black-box models like bias or susceptibility to adversarial attacks. Explainability can help those working with AI to understand how machine learning models arrive at predictions, which can be as simple as understanding which features drive model decisions but more difficult when trying to explain complex models.

Evolution of AI models

Why the push for explainable AI? Models werent always this complex. In fact, lets start with a simple example of a thermostat in winter. The rule-based model is as follows:

Is the thermostat working as expected? The variables are current room temperature and whether the heater is working, so it is very easy to verify based on the temperature in the room.

Certain models, such as temperature control, are inherently explainable due to either the simplicity of the problem, or an inherent, common sense understanding of the physical relationships. In general, for applications where black-box models arent acceptable, using simple models that are inherently explainable may work and be accepted as valid if they are sufficiently accurate.

However, moving to more advanced models has advantages:

Figure 1: Evolution of AI models. A simple model may be more transparent, while a more sophisticated model can improve performance.

Why Explainability?

AI models are often referred to as black-boxes, with no visibility into what the model learned during training, or how to determine whether the model will work as expected in unknown conditions. The focus on explainable models aims to ask questions about the model to uncover any unknowns and explain their predictions, decisions, and actions.

Complexity vs. Explainability

For all the positives about moving to more complex models, the ability to understand what is happening inside the model becomes increasingly challenging. Therefore, engineers need to arrive at new approaches to make sure they can maintain confidence in the models as predictive power increases.

Figure 2: The tradeoff between explainablility and predictive power. In general, more powerful models tend to be less explainable, and engineers will need new approaches to explainability to make sure they can maintain confidence in the models as predictive power increases.

Using explainable models can provide the most insight without adding extra steps to the process. For example, using decision trees or linear weights can provide exact evidence as to why the model chose a particular result.

Engineers who require more insight into their data and models and are driving explainability research for:

Current Explainability Methods

Explainable methods fall into two categories:

Figure 3: The difference between global and local methods. Local methods focus on a single prediction, while global methods focus on multiple predictions.

Understanding feature influence

Global methods include feature ranking, which sorts features by their impact on model predictions, and partial dependence plots, which home in on one specific feature and indicate its impact on model predictions across the whole range of its values.

The most popular local methods are:

Visualizations

When building models for image processing or computer vision applications, visualizations are one of the best ways to assess model explainability.

Model visualizations: Local methods like Grad-CAM and occlusion sensitivity can identify locations in images and text that most strongly influenced the prediction of the model.

Figure 4: Visualizations that provide insight into the incorrect prediction of the network.

Feature comparisons and groupings: The global method T-SNE is one example of using feature groupings to understand relationships between categories. T-SNE does a good job of showing high-dimensional data in a simple two-dimensional plot.

These are only a few of the many techniques currently available to help model developers with explainability. Regardless of the details of the algorithm, the goal is the same: to help engineers gain a deeper understanding about the data and model. When used during AI modeling and testing, these techniques can provide more insight and confidence into AI predictions.

Beyond Explainability

Explainability helps overcome an important drawback of many advanced AI models and their black-box nature. But overcoming stakeholder or regulatory resistance against black-box models is only one step towards confidently using AI in engineered systems. AI used in practice requires models that can be understood, that were constructed using a rigorous process, and that can operate at a level necessary for safety-critical and sensitive applications.

Continuing areas of focus and improvement include:

Is Explainability Right for Your Application?

The future of AI will have a strong emphasis on explainability. As AI is incorporated into safety-critical and everyday applications, scrutiny from both internal stakeholders and external users is likely to increase. Viewing explainability as essential benefits everyone. Engineers have better information to use to debug their models to ensure the output matches their intuition. They gain more insight to meet requirements and standards. And, theyre able to focus on increased transparency for systems that keep getting more complex.

Views expressed above are the author's own.

END OF ARTICLE

Link:

Explainable AI - Times of India

When you plunk a $100 million wind farm down on 98,000 acres of varying terrain, you want to know a few things. You want to know that youre optimizing the location of your multi-million-dollar turbines. You want to know that the turbines you source can handle the gustiest gust of wind they will ever encounter without shattering dramatically in some loudmouth YouTubers viral video. And you want to test potential uses cases and changes in software, which is cheap and changeable, rather than in hardware, which is expensive and notoriously hard to edit.

Which is why Siemens Gamesa, the global renewable energy company, is working with NVIDIA to generate AI-powered digital twins of its turbines.

A screen capture from a video of a wind farm in operation.

What we want to do in moving into the digital twin space is to be able to have an accurate digital model of the entire wind farm where we can play out scenarios, Greg Oxley, a lead data scientist at Siemens told me in a recent TechFirst podcast. This could be ... incoming weather events and we want to see how to optimally operate that wind farm as we move through these type of events. We could be testing new control strategies or something that we want to look moving to the future and we want to see how the wind farm will perform under those new control paradigms.

Siemens has thousands of turbines around the globe that together produce over 100 gigawatts of wind power, enough, the company says, to power 87 million homes every year. Thats enough to want to optimize how they function, and protect them in case of storms.

Shutting them down if theres going to be wind thats too strong is not a step to take lightly it cuts power generation but its also critical to protect expensive infrastructure. That requires dealing with the ughknowns, Oxley says, and its critical to get it right.

Were always trying to mitigate what we dont know and put in the appropriate buffers ... but that puts us in a non-ideal situation, he says. We would rather clear that out and understand as best as possible the unknowns, and get to the true optimization instead of just adding buffers on top of everything.

In other words, adding margin for safety is both good and bad. Its good when it saves money by not destroying turbines, but its bad when it results in unnecessary shut-downs that cost money. Digital twins help Siemens get a truer understanding of their equipment, its capabilities and limits, and gives the company the data and models it needs to be able to react optimally in productive ways.

Thats getting easier to do, says Dion Harris, a product manager at NVIDIA. NVIDIAs latest chips and AI frameworks are accelerating simulation modeling up to 4,000X faster than traditional ways, the company says.

We were only using 22 GPU-accelerated nodes and we were able to deliver the performance of roughly about ... 984,000 nodes on a specific system, Harris told me. Its really about how can you simulate these massively complex environments, but in a very efficient way possible. Because if money was no object, if power was no object, you can just throw CPUs at it all day and you can get there ... AI is giving us some tools to model these very complex systems in a very efficient way both in terms of time and energy efficiency.

NVIDIA is helping to build digital twins of Siemens wind farms using NVIDIA Omniverse, a 3D design technology to connect and create digital worlds, and NVIDIA Modulus, a neural network framework that blends the power of physics in the form of governing partial differential equations with data to build high-fidelity, parameterized surrogate models with near-real-time latency.

Translation: using AI to model the real world at high resolution and making it available not just as tables of data in a spreadsheet, but as a visual, explorable experience.

What's the result of all this super high-tech VR-ish metaversy gamification of renewable energy systems? Fewer known unknowns, and fewer unknown unknowns.

What this allows us to do is really get rid of the unknown, Oxley says.

Within reason, of course. As always, in modeling large-scale physical reality, the question is how you ensure that your model is both accurate to existing real-world systems in all their near-infinite complexity, and predictive of future events.

Which essentially, Oxley says, comes back to boots on the ground. Plus incessant fine-tuning of artificially intelligent knobs and dials.

Were always benchmarking back and forth and actively, he says. So youre actively always in a physics-based model, turning the knobs that you need to get across a wide range [with] the ... least error with whats actually happening in the field. Now the same thing with machine learning models, youre constantly training, theyre constantly improving. So you need this feedback from actual performance in the field, the reality of whats happening, feeding back to your original predictions and tuning back and forth all the time.

Subscribe to TechFirst; get a transcript of our conversation.

Read the original:

Digital Twin Wind Farms: Siemens And NVIDIA Are Modeling Reality With AI In The Metaverse - Forbes

A new study shows that humans can learn new things from artificial intelligence systems and pass them to other humans, in ways that could potentially influence wider human culture.

The study, published on Monday by a group of researchers at the Center for Human and Machines at the Max Planck Institute for Human Development, suggests that while humans can learn from algorithms how to better solve certain problems, human biases prevented performance improvements from lasting as long as expected. Humans tended to prefer solutions from other humans over those proposed by algorithms, because they were more intuitive, or were less costly upfronteven if they paid off more, later.

"Digital technology already influences the processes of social transmission among people by providing new and faster means of communication and imitation," the researchers write in the study. "Going one step further, we argue that rather than a mere means of cultural transmission (such as books or the Internet), algorithmic agents and AI may also play an active role in shaping cultural evolution processes online where humans and algorithms routinely interact."

The crux of this research rests on a relatively simple question: If social learning, or the ability of humans to learn from one another, forms the basis of how humans transmit culture or solve problems collectively, what would social learning look like between humans and algorithms? Considering scientists dont always know and often cant reproduce how their own algorithms work or improve, the idea that machine learning could influence human learningand culture itselfthroughout generations is a frightening one.

"There's a concept called cumulative cultural evolution, where we say that each generation is always pulling up on the next generation, all throughout human history," Levin Brinkmann, one of the researchers who worked on the study, told Motherboard. "Obviously, AI is pulling up on human historythey're trained on human data. But we also found it interesting to think about the other way around: that maybe in the future our human culture would be built up on solutions which have been found originally by an algorithm."

One early example cited in the research is Go, a Chinese strategy board game that saw an algorithmAlphaGobeat the human world champion Lee Sedol in 2016. AlphaGo made moves that were extremely unlikely to be made by human players and were learned via self-play instead of analyzing human gameplay data. The algorithm was made public in 2017 and such moves have become more common among human players, suggesting that a hybrid form of social learning between humans and algorithms was not only possible but durable.

We already know that algorithms can and do significantly affect humans. Theyre not only used to control workers and citizens in physical workplaces, but also control workers on digital platforms and influence the behavior of individuals who use them. Even studies of algorithms have previewed the worrying ease with which these systems can be used to dabble in phrenology and physiognomy. A federal review of facial recognition algorithms in 2019 found that they were rife with racial biases. One 2020 Nature paper used machine learning to track historical changes in how "trustworthiness" has been depicted in portraits, but created diagrams indistinguishable from well-known phrenology booklets and offered universal conclusions from a dataset limited to European portraits of wealthy subjects.

I don't think our work can really say a lot about the formation of norms or how much AI can interfere with that, Brinkmann said. We're focused on a different type of culture, what you could call the culture of innovation, right? A measurable value or peformance where you can clearly say, 'Okay this paradigmlike with AlphaGois maybe more likely to lead to success or less likely."

For the experiment, the researchers used transmission chains, where they created a sequence of problems to be solved and participants could observe the previous solution (and copy it) before solving it themselves. Two chains were created: one with only humans, and a hybrid human-algorithm one where algorithms followed humans but didn't know if the previous player was a human or algorithm.

The task to solve was to find "an optimal sequence of moves" to navigate a network of six nodes and receive awards with each move.

As expected, we found evidence of a performance improvement over generations due to social learning, the researchers wrote. Adding an algorithm with a different problem-solving bias than humans temporarily improved human performance but improvements were not sustained in following generations. While humans did copy solutions from the algorithm, they appeared to do so at a lower rate than they copied other humans solutions with comparable performance.

Brinkmann told Motherboard that while they were surprised superior solutions weren't more commonly adopted, this was in line with other research suggesting human biases in decision-making persist despite social learning. Still, the team is optimistic that future research can yield insight into how to amend this.

"One thing we are looking at now is what collective effects might play a role here," Brinkmann said. "For instance, there is something called 'context bias.' It's really about social factors which may also play a role, about unintuitive or alien solutions for a group can be sustained. We are also quite excited about the question of communication between algorithms and humans: what does that actually look like, what kind of features do we need from AI to learn or imitate solutions from AI?"

Link:

AI Inventing Its Own Culture, Passing It On to Humans, Sociologists Find - VICE

Group photo of inaugural World AI Awards winners with jury members. Supplied photos

Published: Sat 28 May 2022, 6:19 PM

Last updated: Sat 28 May 2022, 6:22 PM

The two-day 35th global edition of World AI Show & Awards, the longest running AI series, organised by Trecon, closed on a high note with the inaugural World AI Awards to recognise contributions in various fields on Thursday.

Held under the patronage of the Private Office of Sheikh Saeed Bin Ahmed Al Maktoum, a member of the royal family of Dubai, the World AI Show and Awards brought together the most powerful voices in the global AI and RPA space.

Dubai has always welcomed talent and we always looking to create a better future, so it was only natural for us to be associated with this event, said Hisham Al Gurg, CEO at the Private Office. AI is the future and it is nice to see so many experts and leading companies at World AI Show & Awards.

Aditya Bhagat, Affinsys co-founder with the Startup World Cup award.

The second day of the World AI Show kick-started with a panel discussion on Women in AI. The confluence of global AI women leaders equivocally stressed the significance for women to prove their worth and get recognition on merit. There was a broader representation of views with panelists from the UAE, Germany, and Oman on stage.

James Connolly, the regional director of DarkTrace, spoke about using artificial intelligence to surgically respond to fast and furious attacks and the threat landscape in 2022.

Padam Kafle, the Aster Hospitals head of IT and Automation, touched on the growth in enterprise adoption of the 5G network. Kafle took the award for best project implementation under the best AI use-case award in the private sector on behalf of Aster.

Other sessions included topics such as ethical uses of AI, ways for successful rapid implementation, trends, and scaling the adoption of AI to deliver business outcomes.

Fahad Alhamed receiving the AI Visionary of the Year award from Roula Moussa, CEO of AI Venture Labs.

A special award was handed out to Eng Fahad A Alhamed, the Founder and Chairman of the Saudi Cloud Computing Association, who was given the AI Visionary of the Year award.

Emirates NBD Bank had three innovations shortlisted in the banking category and the team representing faced some nervous moments before knowing that it was the model they created for ensuring compliance with financial regulations using a quantitative scoring system with scorecards to identify bottlenecks.

In addition, they have created a call center analytics that uses AI to predict consumer behavior, as well as a Multiplier effect that gives the next-best action and personalization for a better banking experience. With his ENBD colleagues, Sachin Chandna received the best AI use case award private sector in the banking segment along with his head of customer intelligence who received the AI proponent of the year award in the private sector as well.

The opening day saw the UAE finalist being spotted for the Startup World Cup. Affinsys, a startup running AI solutions for banks, telecom and insurance companies, won after the jury took a live decision based on final pitches from the top contenders and voted for UAE's representation among 60-plus countries from where the World Cup winner will receive $1 million funding.

The inaugural edition of the World AI Awards was hosted with many AI visionaries and true flag bearers of the Mena region receiving the award for their contribution in the field of artificial intelligence. Below is the list of all the AI Awardee winners:

Winners of World AI Awards:

Best AI use-case in Private Sector:

Healthcare Mr. Aliasgar Bohari, Zulekha Hospital

Education Shameed Sait, GEMS Education

Banking Sachin Chandna, Emirates NBD

Financial Services Saurav Suman, The Family Office

Manufacturing Wissam Al Adany, ADES Arabia Holding

Retail Mustafa Hussein, IKEA, Saudi Arabia

Best Project Implementation Padam Kafle, Aster Hospitals

Best AI use-case in Public Sector:

Healthcare - Dr Maryam Alsharaf, Emirates Health Services

Best Project Implementation - Dr Maryam Alsharaf, Emirates Health Services

Education Saud Aldajah, Higher Colleges of Technology

Energy & Utilities Awad El-Sidiq, Adnoc Distribution

Others

Best Young Role Model Maya Hiba, Al Ramz Corporation

Best Value Chain - Raman Nathani, Rafed Pure Health

Most Popular Proponent of the Year Sachin Chandna, Emirates NBD Bank

Visionary of the Year Fahad Alhamed, Saudi Cloud Computing Association chairman. business@khaleejtimes.com

Originally posted here:

World AI Show and Awards end on a high note by celebrating achievers in AI - Khaleej Times

Microsoft for Startups Israel has announced 15 startups that have been selected for the third AI For Good Accelerator Program, in collaboration with Tech for Good. The startups, all working with artificial intelligence, come from the fields of sustainability, agritech, and healthcare. Microsoft for Startups has supported more than 1,000 startups to date.

Each participant will receive individual mentorship guidance from experienced mentors for business and technological development, access to Microsofts advanced tools, Foundation, assistance building business plans, and opportunities to establish connections with potential customers, investors, and more.

1 View gallery

MSFT for startups team.

(Michael Tomarkin)

Among the mentors are Hamutal Meridor, Partner at Vintage Investment Partners; Yuda Doron, Managing Partner at Greenfield Partners; and Guy Franklin, Founder and General Partner at Israeli Mapped in NY Ventures. Participants in AI for Goods previous programs have gone on to become some of Israels most successful high-tech companies, including Pecan, an automated, predictive analytics platform that processes large amounts of data of various types from various sources, and Clarifruit, which developed an automated quality control and data analysis system for fresh produce.

This year, most of the participating companies specialize in innovative technologies that foster sustainability and environmental preservation, one of the most pressing issues facing todays world in light of the worsening climate crisis. Microsofts support will enable these startups to improve their technology, create international connections and fully realize their potential, said Raz Bachar, Managing Director, Microsoft for Startups Israel.

We were glad to see the tremendous response of companies interested in taking part in the accelerators third session and were certain that this years participants will join the list of success stories from the first two sessions, with whom our connection continues to deepen.

The 15 companies taking part in the third AI For Good Accelerator Program are:

Trellis - which is an AI-powered food system intelligence platform that enables food producers, traders and supply chain operators to plan and manage risks in the global food chain.

Strasa.tech - which supplies high-resolution aerial imagery at low prices, and facilitates internet connections in areas without communications infrastructure. The company uses autonomous, solar-powered, lighter-than-air vehicles that hover in the lower stratosphere and produce no pollution or carbon emissions.

BioShade - which develops technology to solve the problem of urban heat islands using a smart, modular hydroponic system customized for growing shade plants on rooftops and balconies, or in areas where it is difficult to plant trees due to the presence of underground infrastructures.

QRGenetics - which developed an AI-based drug discovery engine that identifies potential treatments for common genetic diseases by uncovering new treatments for individual patients with similar rare disease mutations.

Hexa - which developed a platform that can convert a single image into immersive 3D models that create, manage, distribute and analyze complex visualization projects.

Agrinoze - which has developed an autonomous system for irrigation and fertilizing crops that increases yields by hundreds of percentage points, while at the same time creating savings of 50% on water and 70% on fertilizer.

BeeFreeAgro - which is changing the way livestock is raised with its AI-based, autonomous drone herding system that promotes the animals wellbeing and increases food safety and sustainability, while reducing farmers carbon footprint.

GeoX - which developed AI-based technology that enables the 3D analysis of aerial photographs for the automatic generation of highly accurate property intelligence, which is expected to contribute greatly to the property insurance industry.

HAAT - which is a food delivery platform that specializes in towns without specific street addresses, and restaurants without smart cash registers or digital menus.

MAOLAC - which combines proteomics and NLP (search algorithms) to identify and create functional milk proteins (FMPs) that have a close biological resemblance to breast milk, the best source of nutrition for the entire body, to improve the health and wellbeing of humans and animals.

Emnotion - which is a next-generation global climate modeling platform designed to prepare users for future extreme climate events.

Neolithics - which uses AI-based systems to inspect the quality and condition of fresh fruits and vegetables in the food supply chain.

Viridian - which provides extensive environmental, climate and ESG information of companies, sectors and regions, for financial players, using big data and AI technologies, in order to reduce climate and transition risk, identify business opportunities and comply with regulations.

OtailO - which provides an end-to-end, smart and sustainable solution for the management of online product returns and exchanges, and processes hundreds of thousands of returns on a regular basis. The companys solution produces de facto reduction of carbon emissions resulting from merchandise returns and cuts shipping costs for retailers.

Brightmerge - which is developing a cloud-based platform that supports and accelerates effective decision-making for the renewable energy industry.

First published: 10:32, 24.05.22

See the rest here:

Microsoft announces third cycle of AI For Good Accelerator Program - CTech