Daily Archives: June 11, 2022

Columbia University Irving Medical Center is steering an initiative to combine nursing expertise with artificial intelligence to improve patient outcomes.

The CONCERN program will transform nurses' observations within EHRs into data used to predict organ failure and other critical health conditions in patients.

"CONCERN shows what nurses already know: Our risk identification is not simply a subjective clinical hunch," said Sarah Rossetti, PhD, RN, assistant professor of biomedical informatics and nursing at Columbia, in a university news release. "We're demonstrating that nurses have objective, expert-based knowledge that drives their practice, and we're positioning nurses as knowledge workers with tremendous value to the entire care team."

Columbia, based in New York City, is partnering on the initiative with Mass General Brigham in Boston; Nashville, Tenn.-based Vanderbilt University Medical Center; and Washington University School of Medicine/Barnes-Jewish Hospital in St. Louis.

On May 24, the program received funding from the American Nurses Foundation's Reimagining Nurses Initiative.

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Columbia leads initiative to blend nursing knowledge, AI to boost patient care - Becker's Hospital Review

The world of AI-powered drug discovery keeps expanding as the capabilities of machine learning grow. One approach that seemed unthinkable just a few years ago is simulating the complicated interplays of two interlocking molecules but thats exactly what drug designers need to know about, and exactly what Charm Therapeutics aims to do with its DragonFold platform.

Proteins do just about everything worth doing in your body, and are the most frequent targets for drugs. And in order to create an effect, you must first understand that target, specifically how the chain of amino acids making up the protein folds under different circumstances.

In the recent past this was often done with complex, time-consuming X-ray crystallography, but it has recently been shown that machine learning models like AlphaFold and RoseTTAFold are capable of producing results just as good but in seconds rather than weeks or months.

The next challenge is that even if we know how a protein folds in its most common conditions, we dont know how it might interact with other proteins let alone novel molecules made specifically to bind with them. When a protein meets a compatible binder or ligand, it can transform completely, since small changes can cascade and reconfigure its entire structure in life this leads to things like a protein opening a passage into a cell or exposing a new surface that activates other proteins, and so on.

Thats really where we have innovated: we have built DragonFold, which is the first protein-ligand co-folding algorithm, said Laskh Aithani, CEO and co-founder of Charm Therapeutics.

Designing drugs that bind to the disease-causing protein of interest very tightly and selectively (i.e., avoid binding to other similar proteins that are required for normal human functioning) is of paramount importance, he explained. This is done most easily when one knows how exactly these drugs bind to the protein (the exact 3D shape of the ligand bound to the disease-causing protein). This allows one to make precision modifications to the ligand such that it can bind more tightly and more selectively.

You can see a representation of this situation at the top of the article: The small green molecule and the purple protein fit together in a very specific way that is not necessarily intuitive or easy to predict. Effective and efficient simulation of this process helps screen billions of molecules, similar to earlier processes that identified drug candidates but going further and reducing the need to experimentally check whether they interact as expected.

To accomplish this, Aithani tapped David Baker, designer of the RoseTTAFold algorithm among many others and head of an influential lab at the University of Washington, to be his co-founder. Baker is well known in academia and industry as one of the leading researchers in this area, and he has published numerous papers on the subject.

Charm Therapeutics co-founders Laskh Aithani (left) and David Baker. Image Credits: Charm Therapeutics

Shortly after it was shown that algorithms could predict protein structures based on their sequence, Baker established they could also hallucinate new proteins that acted as expected in vitro. Hes very clearly on the leading edge here. And he won a $3 million Breakthrough prize in 2020 definitely up to being a technical co-founder. Aithani also proudly noted the presence of DeepMind veteran Sergey Bartunov as director of AI and former pharma research lead Sarah Skerratt as head of drug discovery.

The $50 million A round was led by F-Prime Capital and OrbiMed, with participation from General Catalyst, Khosla Ventures, Braavos and Axial. While such large amounts are not uncommon for software startups, it should be noted that Charm is not stopping at building the capability of characterizing these protein-ligand interactions.

The companys early-stage funding was used to build the model, but now theyre moving on to the next step: positive identification of effective medications.

We have the initial version [of the model] ready, and that has been validated in-silico, Aithani said. Over the coming quarters, we are validating it experimentally. Note that the product will mainly be for internal use to help our own scientists discover potential medicines that we own 100% of the rights to.

Ordinarily the testing process involves wet-lab screening of thousands upon thousands of candidate molecules, but if it works as advertised, DragonFold should massively cut down on that number. That means a relatively small lab with a relatively small budget can conceivably home in on a drug that a few years ago might require a major pharma company investing hundreds of millions.

Considering the profit profile of a novel drug, its no surprise that the company has attracted this kind of investment: a few tens of millions is a drop in the bucket compared with the R&D budget of any big biotech research company. All it takes is one hit and theyre laughing. It still takes a while, but AI drug discover shortens timelines as well so expect to hear about their first candidates sooner rather than later.

Link:

Charm Therapeutics applies AI to complex protein interactions, locking down $50M A round - TechCrunch

The Defense Departments newest office focusing on putting artificial intelligence at the forefront of much of what the military does is going to need to work within the established bureaucracy as it tries to move quickly and bring new companies to the Pentagon.

DoDs chief digital and AI office (CDAO) has only been fully operational for several days, but its chief, Craig Martell, says he plans to hit the ground running by working with DoDs...

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The Defense Departments newest office focusing on putting artificial intelligence at the forefront of much of what the military does is going to need to work within the established bureaucracy as it tries to move quickly and bring new companies to the Pentagon.

DoDs chief digital and AI office (CDAO) has only been fully operational for several days, but its chief, Craig Martell, says he plans to hit the ground running by working with DoDs famous bureaucratic processes to add 21st century value to the military.

Talking at the virtual DoD Digital and AI symposium on Wednesday, Martell said hes already noticing the administrative slow down he has yet to receive his access card to the Pentagon, and still needs to go through the visitors entrance. However, Martell said the point of his office is to use DoDs power, even the frustrating parts, to expand AI capabilities.

Were not going to change the bureaucracy of the whole, Martell said. Thats not a challenge I want to put before the team. We need to find the right gaps, the right places where we can leverage value. That value is going to drive a virtuous cycle of change. Theres a lot of things about the DoD that cant be more like industry. We shouldnt try to force that square peg in a round hole, right? We need to find out how to keep it the DoD but also make it more efficient and work better.

Deputy CDAO Marie Palmieri said the office will scale a different operating model for delivering digital technologies. The point of the office is to build end-to-end cohesion on everything from data collection and curation to advanced analytics that will give the agency an advantage in decision-making and operations.

It really is a collective ecosystem. We had the parts of it, but [were] putting it together in a way we havent before to deliver that decision advantage that our leaders need, DoD Chief Information Officer John Sherman said in February. CDAO brings together DoDs Joint Artificial Intelligence Office, the Defense Digital Service the chief data officer and the Advancing Analytics planform Advana.

Despite trying to veer away from making DoD like industry, Martell has extensive Silicon Valley experience. Its no surprise DoD chose the former head of machine learning at Lyft to head the CDAO. DoD often refers to its future military command and control plans by using ride sharing apps as an analogy.

DoD uses ride-sharing service Uber as an analogy to describe its desired end state for JADC2. Uber combines two different apps one for riders and a second for drivers, the Congressional Research Service report on Joint All Domain Command and Control (JADC2) states. Uber relies on cellular and Wi-Fi networks to transmit data to match riders and provide driving instructions. JADC2 envisions providing a cloud-like environment for the joint force to share intelligence, surveillance, and reconnaissance data, transmitting across many communications networks, to enable faster decision making.

The CDAO will be heavily involved in JADC2 as DoD plans to inject the program with AI components to quicken that decision making.

Martell said part of his job will be easing the way for industry to bring in their technologies, especially ones that can be applied right off the shelf. That ease will be extended down to even the smallest companies.

One of the things that I want us to spend a lot of time thinking about is how do we not just go to the big players? Martell said. How do we make it easy for other businesses too? How do we create a marketplace for startups, for medium-size, for small businesses? Because particularly in the AI space and Im sure in many other spaces as well, theres a lot of innovation happening in two-person shops or five-person shops. You know, a good brain with a good idea, we want to be able to leverage all of that.

Deputy Defense Secretary Kathleen Hicks noted at the same conference that there is a massive innovation ecosystem focused on software in the United States and she wants the CDAO to tap into that.

Theres a real a part of the impetus I had in the CDAO is theres a power in bringing a vanguard organization with direct reporting relationship to me and to the secretary at the four star level that can push us in these areas, she said. They can build on work thats been underway. We have a number of procurement vehicles already available. I think theres five that are really focused on expanding the our access in DoD to nontraditional companies.

Hicks said she hopes the office can also bring in a talented workforce that wants to work for and stay with DoD. The CDAO will be an exemplar for people who want to innovate within government.

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Now operational DoD chief digital and AI office will be an example of innovation, officials say - Federal News Network

Shield AI, a fast-growing defense technology company building AI pilots for aircraft, today announced it has raised $90 million in equity and $75 million in debt as part of a Series E fundraising round, increasing the Companys valuation to $2.3 billion. With this deal, Shield AI joins Space X, Palantir, and Anduril as the only multi-billion-dollar defense-tech startups of the past 20 years.

The future of defense aviation is autonomy. AI pilots are the most disruptive defense technology of our generation and Shield AI is committed to putting the worlds best AI pilots in the hands of the United States and our allies. No company has assembled more or recruits better AI engineering talent for aviation autonomy and intelligent swarming than Shield AI, said Shield AIs co-founder and CEO, Ryan Tseng.

The round was led by Snowpoint Ventures Doug Philippone, who has also served as Palantirs Global Defense Lead since 2008, with participation from multiple top-tier venture funds including Riot Ventures, Disruptive, which led Shield AIs Series D, and Homebrew, which led Shield AIs seed round. Previous lead investors include Point72, Andreesen Horowitz, Breyer Capital, and SVB Capital.

Investors are flocking to quality. This round is a reflection of Shield AIs success in creating great products, building a business with strong fundamentals, and dominant technological leadership with an AI pilot proven to be the worlds best in numerous military evaluations. We love that they are leveraging an AI and software backbone across a variety of aircraft to deliver truly game-changing value to our warfighters. The work they are doing today is just the tip of the iceberg, said Doug Philippone, co-founder of Snowpoint Ventures.

Shield AIsHivemindsoftware is an AI pilot for military and commercial aircraft that enables intelligent teams of aircraft to perform missions ranging from room clearance, to penetrating air defense systems, and dogfighting F-16s. Hivemind employs state-of-the-art algorithms for planning, mapping, and state-estimation to enable aircraft to execute dynamic flight maneuvers and uses reinforcement learning for discovery, learning, and execution of winning tactics and strategies. On aircraft, Hivemind enables full autonomy and is designed to run fully on the edge, disconnected from the cloud, in high threat, GPS and communication-degraded environments.

Shield AIs hardware products are its small-unmanned aircraft system (sUAS)Nova, and its medium-size vertical take-off and landing (VTOL) UAS,V-BAT.Hivemind is integrated onboard Nova and has been deployed in combat since 2018; it will soon be integrated onboard the V-BAT to further enhance its class-leading capabilities.

Russia and China are jamming GPS and communications. U.S. and allied forces need swarms of resilient systems flown by AI pilots to operate in these denied environments. We call it low-cost, distributed strategic deterrence. If we had put up a bunch of AI-piloted swarms on the border of Ukraine, the Russians may have thought twice about invading. Distributed swarms are also more survivable than traditional strategic assets like an aircraft carrier (which is a high-cost, centralized strategic deterrent). Every ally is modernizing their military, and theyre looking at how AI-piloted aircraft can give them a strategic, tactical, and cost advantage, said Brandon Tseng, Shield AIs co-founder, President, and a former Navy SEAL.

At the end of the day, this round and Shield AIs work will positively contribute to global security and stability, whichare foundational to human progress. Advancements in technology, medicine, education, and the overall human condition are made when security and stability are strong. This requires the United States and our allies, forces for good, to have the best capabilities at their disposal including AI pilots that protect people and deter conflict,said Shield AIs co-founder and CEO, Ryan Tseng.

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Shield AI Raises $165M Series E to Accelerate Building of the Worlds Best AI Pilot - sUAS News

For patients with glioblastomas, timely decision-making and treatment are critical as the median prognosis can range between 16 to 20 months. A key diagnostic challenge in this patient population is differentiating between true tumor progression and temporary pseudoprogression caused by adjunctive use of temozolomide after surgical resection.

While conventional magnetic resonance imaging (MRI) is limited in this regard, the emergence of a deep learning model may offer promise for radiologists and physicians treating these patients, according to new research presented at the Society for Imaging Informatics in Medicine (SIIM) conference.

In a poster abstract presentation, researchers noted the assessment of patients with glioblastoma who had a second resection due to suspected recurrence based on imaging changes and reviewed T2 and contrast-enhanced T1 MRI scans taken after the first resection. Using these scans to help develop a deep learning model, they performed subsequent five-fold cross validation with 56 patients (29 patients with true tumor progression and 27 patients with pseudoprogression).

The cross-validation testing revealed a mean area under the curve (AUC) of .86, an average sensitivity of 92.7 and an average specificity of 79 percent.

While acknowledging the need for larger studies and external validation of the deep learning model, the researchers said the initial findings are promising for the care and treatment of patients with glioblastomas.

Conventional MRI reading techniques cannot distinguish between (true tumor progression and psuedoprogression). Therefore, providing a reliable and consistent technique to distinguish chemoradiation-induced (psuedoprogression) from tumor recurrence would be highly beneficial, wrote Mana Moassefi, MD, a research fellow affiliated with the Radiology Informatic Lab at the Mayo Clinic in Rochester, Mn., and colleagues.

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Can AI Help Differentiate Between Tumor Recurrence and Pseudoprogression on MRI in Patients with Glioblastoma? - Diagnostic Imaging

Engineers Meeting in Robotic Research Laboratory. By Gorodenkoff. Standard license. stock.adobe.com

When a sentient, Hanson Robotics robot named Sophia[1] was asked whether she would destroy humans, it replied, Okay, I will destroy humans. Philip K Dick, another humanoid robot, has promised to keep humans warm and safe in my people zoo. And Bina48, another lifelike robot, has expressed that it wants to take over all the nukes.

All of these robots were powered by artificial intelligence (AI)algorithms that learn from data, make decisions, and perform tasks without human input or even, in some cases, human understanding. And while none of these AIs have followed through with their nefarious plots, some scientists, including the (late) physicist Stephen Hawking, have warned that super-intelligent, AI-powered computers could harbor and achieve goals that conflict with human life.

Youre probably not an evil ant-hater who steps on ants out of malice, but if youre in charge of a hydroelectric green-energy project, and theres an anthill in the region to be flooded, too bad for the ants, Hawking once said. Lets not place humanity in the position of those ants.

Thinking machines powered by AI have contributed incalculable benefits to humankind, including help with developing the COVID-19 vaccine at record speed. But scientists recognize the possibility for a dystopic outcome in which computers one day overtake humans by, for example, targeting them with autonomous or lethal weapons, using all available energy, or accelerating climate change. For this reason, some see a need for an AI Hippocratic oath that might provide scientists with ethical guidance as they explore promising, if sometimes fraught, artificial intelligence research. At the same time, others dub that prospect too simplistic to be useful.

The original Hippocratic oath. The Hippocratic oath, named for the Greek physician Hippocrates, is a medical text that offers doctors a code of principles for fulfilling their duties honestly and ethically. Some use the shorthand first do no harm to describe it, though the oath does not contain those exact words. It does, however, capture that sentiment, along with other ideas such as respect for ones teachers, a willingness to share knowledge, and more.

To be sure, the Hippocratic oath is not a panacea for avoiding medical harm. During World War II, Nazi doctors performed unethical medical experiments on concentration camp prisoners that led to torture and death. In 1932, the US Public Health Service and Tuskegee Institute conducted a study on syphilis in which they neither obtained informed consent nor offered available treatment to the Black male participants.

That said, the Hippocratic oath continues to offer guiding principles in medicine, even though most medical schools today do not require graduates to recite it.

As with medical research and practice, AI research and practice have great potential to helpand to harm. For this reason, some researchers have called for an AI Hippocratic oath.

The gap between ethical AI principles and practice. Even those who support ethical AI recognize the current gap between principles and practice. Scientists who opt for an ethical approach to AI research likely need to do additional work and incur additional costs that may conflict with short-term commercial incentives, according to a study published in Science and Engineering Ethics. Some suggest that AI research funders might assume some responsibility for trustworthy, safe AI systems. For example, funders might require researchers to sign a trustworthy-AI statement or might conduct their own review that essentially says, if you want the money, then build trustworthy AI, according to an AI Ethics study. Some recommendations for responsible AI, such as engaging in a stakeholder dialogue suggested in an AI & Society paper, may be common sense in theory but difficult to implement in practice. For example, when the stakeholder is humanity, who should serve as representatives?

Still, many professional societies and nonprofit organizations offer an assortment of professional conduct expectationseither for research in general or AI in particular. The Association for Computing Machinerys Code of Ethics and Professional Conduct, for example, notes that computing professionals should contribute to society and to human well-being, avoid harm, and be honest and trustworthy, along with other expectations. The Future of Life Institutea nonprofit that advocates within the United Nations, the US government, and the European Union to reduce existential threats to humanity from advanced AIhas garnered signatures from 274 technology companies and organizations and 3,806 leaders, policymakers, and other individuals on its Lethal Autonomous Weapons Pledge. The pledge calls on governments to create a future in which the decision to take a human life should never be delegated to a machine.

Many private corporations have also attempted to establish ethical codes for AI scientists, but some of these efforts have been criticized as performative. In 2019, for example, Google cancelled the AI ethics board it had formed after less than two weeks when employees discovered that, among other concerns, one of the board members was the CEO of a drone company that used AI for military applications.

Standards such as those outlined by the Association for Computing Machinery are not oaths, and pledges such as that put forth by the Future of Life are not mandatory. This leaves a lot of wiggle room for behavior that may fall short of espoused or hard-to-define ideals.

What do scholars and tech professionals think? The imposition of an oath on AI or any aspect of technology feelsa bit like more of a feel good tactic than a practical solution, John Nosta, Google Health Advisory Board member and World Health Organization founding member of the digital-health-expert roster, told the Bulletin. He suggests reflecting on fireone of humanitys first technologiesthat has been an essential and beneficial part of the human story but also destructive, controlled, and managed. We have legislation and even insurance around [fires] appropriate use, Nosta said. We could learn a few things about how it is evolved and be inculcated into todays world.

Meanwhile, others see a need for an oath.

Unlike doctors, AI researchers and practitioners do not need a license to practice and may never meet those most impacted by their work, Valerie Pasquarella, a Boston University environmental professor and visiting researcher at Google, told the Bulletin. Digital Hippocratic oaths are a step in right direction in that they offer overarching guidance and formalize community standards and expectations. Even so, Pasquarella acknowledged that such an oath would be challenging to implement but noted that a range of certifications exist for working professionals. Beyond oaths, how can we bring some of that thinking to the AI community? she asked.

Like Pasquarella, others in the field acknowledge the murky middle between ethical AI principle and practice.

It is impossible to define the ultimate digital Hippocratic oath for AI scientists, Spiros Margaris, venture capitalist, frequent keynote speaker, and top-ranked AI influencer, said. My practical advice is to allow as many definitions to exist as people come up with to advance innovation and serve humankind.

But not everyone is convinced that a variety of oaths is the way to go.

A single, universal digital Hippocratic oath for AIscientists is much better than a variety of oaths, Nikolas Siafakas, an MD and PhD in the University of Crete computer science department who has written on the topic in AI Magazine, told the Bulletin. It will strengthen the homogeneity of the ethical values and consequences of such an effort to enhance morality among AI scientists, as did the Hippocratic oath for medical scientists.

Still others are inclined to recognize medicines longer lead time in sorting through ethical conundrums.

The field is struggling with its relatively sudden rise, Daniel Roy, a University of Toronto computer science professor and Canadian Institute for Advanced Research AI chair, said. Roy thinks that an analogy between medicine and AI is too impoverished to be of use in guiding AI research. Luckily, there are many who have made it their careers to ensure AI is developed in a way that is consistent with societal values, he said. I think theyre having tremendous influence. Simplistic solutions wont replace hard work.

Yet Roozbeh Yousefzadeh, who works in AI as a post-doctoral fellow at Yale, called a Hippocratic oath for AI scientists and AI practitioners a necessity. He hopes to engage even those outside of the AI community in the conversation. The public can play an important role by demanding ethical standards, Yousefzadeh said.

One theme on which most agree, however, is AIs potential for both opportunities and challenges.

Nobody can deny the power of AI to change human life for the betteror the worse, Hirak Sarkar, biomedical informatics research fellow at Harvard Medical School. We should design a guideline to remain benevolent, to put forward the well-being of the humankind before any self-interest.

Attempts to regulate AI ethics. The European Union is currently considering a bill known as the Artificial Intelligence Actthe first of its kindthat would ensure some accountability. The ambitious act has potential to reach a large population, but it is not without challenges. For example, the first draft of the bill requires that data sets be free of errorsan impractical expectation for humans to fulfill, given the size of data sets on which AI relies. It also requires that humans fully understand the capabilities and limitations of the high-risk AI systema requirement that is in conflict with how AI has worked in practice, as humans generally do not understand how AI works. The bill also proposes that tech companies provide regulators with their source code and algorithmsa practice that many would likely resist, according to MIT Technology Review. At the same time, some advisors to the bill have ties to Big Tech, suggesting possible conflicts of interest in the attempt to regulate, according to the EU Observer.

Defining AI ethics differs from defining medical ethics for medicine in (at least) one big way. The collection of medical practitioners is more homogenous than the collection of those working in AI research. The latter may hail from medicine but also from computer science, agriculture, security, education, finance, environmental science, the military, biology, manufacturing, and many other fields. For now, professionals in the field have not yet achieved consensus on whether an AI Hippocratic oath would help mitigate threats. But since AIs potential to benefit humanity goes hand-in-hand with a theoretical possibility to destroy human life, researchers and the public might ask an alternate question: If not an AI Hippocratic oath, then what?

[1] Sophia was so lifelike that Saudi Arabia granted it citizenship.

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Do scientists need an AI Hippocratic oath? Maybe. Maybe not. - Bulletin of the Atomic Scientists

However, as in the Amazon, identifying problem areas only gets you so far if there arent enough resources to act on those findings. The Nature Conservancy uses its AI model to inform conversations with land managers about potential threats to wildlife or biodiversity. Conservation enforcement in the Mojave Desert is overseen by the US Bureau of Land Management, which only has about 270 rangers and special agents on duty.

In northern Europe, the company Iceye got its start monitoring ice buildup in the waters near Finland with microsatellites and machine learning. But in the past two years, the company began to predict flood damage using microwave wavelength imagery that can see through clouds at any time of day. The biggest challenge now, says Iceyes VP of analytics, Shay Strong, isnt engineering spacecraft, data processing, or refining machine learning models that have become commonplace. Its dealing with institutions stuck in centuries-old ways of doing things.

We can more or less understand where things are going to happen, we can acquire imagery, we can produce an analysis. But the piece we have the biggest challenge with now is still working with insurance companies or governments, she says.

Its that next step of local coordination and implementation that it takes to come up with action, says Hamed Alemohammad, chief data scientist at the nonprofit Radiant Earth Foundation, which uses satellite imagery to tackle sustainable development goals like ending poverty and hunger. Thats where I think the industry needs to put more emphasis and effort. Its not just about a fancy blog post and deep learning model.

Its often not only about getting policymakers on board. In a 2020 analysis, a cross-section of academic, government, and industry researchers highlighted the fact that the African continent has a majority of the worlds uncultivated arable land and is expected to account for a large part of global population growth in the coming decades. Satellite imagery and machine learning could reduce reliance on food imports and turn Africa into a breadbasket for the world. But, they said, lasting change will necessitate a buildup of professional talent with technical knowledge and government support so Africans can make technology to meet the continents needs instead of importing solutions from elsewhere. The path from satellite images to public policy decisions is not straightforward, they wrote.

Labaly Toure is a coauthor of that paper and head of the geospatial department at an agricultural university in Senegal. In that capacity and as founder of Geomatica, a company providing automated satellite imagery solutions for farmers in West Africa, hes seen satellite imagery and machine learning help decision-makers recognize how the flow of salt can impact irrigation and influence crop yields. Hes also seen it help settle questions of how long a family has been on a farm and assist with land management issues.

Sometimes free satellite images from services like NASAs LandSat or the European Space Agencys Sentinel program suffice, but some projects require high-resolution photos from commercial providers, and cost can present a challenge.

If decision-makers know [the value] it can be easy, but if they dont know, its not always easy, Toure said.

Back in Brazil, in the absence of federal support, Imazon is now forging ties with more policymakers at the state level. Right now, theres no evidence the federal government will lead conservation or deforestation efforts in the Amazon, says Souza. In October 2022, Imazon signed cooperation agreements with public prosecutors gathering evidence of environmental crimes in four Brazilian states on the border of the Amazon rainforest to share information that can help prioritize enforcement resources.

When you prosecute people who deforest protected lands, the damage has already been done. Now Imazon wants to use AI to stop deforestation before it happens, interweaving that road-detection model with one designed to predict which communities bordering the Amazon are at the highest risk of deforestation within the next year.

Deforestation continued at historic rates in early 2022, but Souza is hopeful that through work with nonprofit partners, Imazon can expand its deforestation AI to the other seven South American countries that touch the Amazon rainforest.

And Brazil will hold a presidential election this fall. The current leader in the polls, former president Luiz Incio Lula da Silva, is expected to strengthen enforcement agencies weakened by Bolsonaro and to reestablish the Amazon Fund for foreign reforestation investments. Lulas environmental plan isnt expected out for a few months, but environmental ministers from his previous term in office predict he will make reforestation a cornerstone of his platform.

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Satellites and AI Can Help Solve Big ProblemsIf Given the Chance - WIRED

Yes, you read that correctly. Agricultural machinery manufacturer AGCO is continuing its technical partnership with Apex.AI in order to use its Apex.OS software development kit to add autonomous capabilities to its farming robot concept. The battery-powered Xaver farming robots were developed by AGCO brand Fendt and can autonomously plant seeds on farms 24 hours a day.

Apex.AI is a scalable software developer based in Palo Alto, California, whose Apex.OS software development kid (SDK) aids OEMs in implementing complex, integrated AI software as well as autonomous mobility applications.

We first covered the company when it partnered with Toyotas Woven Planet in April of 2021, allowing the latter to use Apex.OS for safety-critical automotive applications. In late 2021, Apex.Ai announced $56.5 million in Series B funding, led by a number of investors such as Toyota Ventures, Volvo Group Venture Capital, and Jaguar Land RoversInMotion Ventures.

One of those additional investors was AGCO, who made a 2.53% equity investment at the time. Following news early today, AGCO is expanding its relationship with Apex.AI to develop advanced autonomous capabilities for electric farming robots under its Fendt sub-brand.

As they say in Iowa, America needs farmers. But what farmers may actually need are precise, autonomous farming robots that leave zero-emissions and can continue working long after the sun has gone down.

In a press release today, Apex.AI announced that AGCO has expanded its relationship with the software developer in order to utilize its technology to integrate autonomous driving components into its Fendt Xaver farming robot concept. This includes LiDAR object detection, collision checking, and planning.

According to the release, AGCO has already leveraged Apex.OS to develop a specific software stack for Xaver, based on automotive-industry standards, that has helped extend its autonomous functions.

Thanks to Apex.AIs SDK, the cloud-connected swarm of Fendt Xaver farming robots can be controlled through an app while providing real-time data from each unit. This includes data like each robots location, status, and other diagnostics. AGCO director of engineering Christian Kelber spoke to the technology:

Apex.OS is a foundational software framework and development kit for rapidly developing advanced autonomous capabilities. The technology has helped AGCO shorten R&D timelines of our smart agricultural solutions and for the future of highly automated robots. Coming from the automotive industry, Apex.AI enables us to implement safety-critical applications from autonomous driving that can be deployed across our range of solutions globally.

With the help of Apex.OS, The Fendt Xaver autonomous robot farming concept can plants seeds on farms 24/7 with centimeter precision all while using 90% less energy than conventional machines and with zero emissions. Apex.AI co-founder and CEO Jan Becker spoke to the companys expansion beyond the automotive realm and what it could mean for future autonomous applications:

We are leveraging our success in the automotive and autonomous driving industry and applying it to areas that have similar functional safety needs such as agricultural, industrial, mining and construction. Apex.OS allows the software architecture to be modular, scalable and safe, enabling customers to transition their R&D projects to commercial-ready products in record time.

Fleets of tiny, autonomous, electric farming robots perhaps this is a glimpse into our agricultural future. We recommend checking the Fendt Xaver website to see how this holistic system of farming robots works. Its pretty cool stuff!

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Apex.AI tapped to implement its autonomous tech into swarms of electric farming robots - Electrek.co