Daily Archives: March 5, 2020

AI can be used for the early detection of virus outbreaks that might result in a pandemic. (Photo by ... [+] Emanuele Cremaschi/Getty Images)

AI detected the coronavirus long before the worlds population really knew what it was. On December 31st, a Toronto-based startup called BlueDot identified the outbreak in Wuhan, several hours after the first cases were diagnosed by local authorities. The BlueDot team confirmed the info its system had relayed and informed their clients that very day, nearly a week before Chinese and international health organisations made official announcements.

Thanks to the speed and scale of AI, BlueDot was able to get a head start over everyone else. If nothing else, this reveals that AI will be key in forestalling the next coronavirus-like outbreak.

BlueDot isn't the only startup harnessing AI and machine learning to combat the spread of contagious viruses. One Israel-based medtech company, Nanox, has developed a mobile digital X-ray system that uses AI cloud-based software to diagnose infections and help prevent epidemic outbreaks. Dubbed the Nanox System, it incorporates a vast image database, radiologist matching, diagnostic reviews and annotations, and also assistive artificial intelligence systems, which combine all of the above to arrive at an early diagnosis.

Nanox is currently building on this technology to develop a new standing X-ray machine that will supply tomographic images of the lungs. The company plans to market the machine so that it can be installed in public places, such as airports, train stations, seaports, or anywhere else where large groups of people rub shoulders.

Given that the new system, as well as the existing Nanox System, are lower cost mobile imaging devices, it's unsurprising to hear that Nanox has attracted investment from funds looking to capitalise on AI's potential for thwarting epidemics. This month, the company announced a $26 million strategic investment from Foxconn. It also signed an agreement this week to supply 1,000 of its Nanox Systems to medical imaging services across Australia, New Zealand and Norway. Coronavirus be warned.

Its CEO and co-founder Ran Poliakine, explains that such deals are a testament to how the future of epidemic prevention lies with AI-based diagnostic tools. "Nanox has achieved a technological breakthrough by digitizing traditional X-rays, and now we are ready to take a giant leap forward in making it possible to provide one scan per person, per year, for preventative measures," he tells me.

Importantly, the key feature of AI in terms of preventing epidemics is its speed and scale. As Poliakine explains, "AI can detect conditions instantly which makes it a great source of power when trying to prevent epidemics. If we talk about 1,000 systems scanning 60 people a day on average, this translates to 60,000 scans that need to be processed daily by the professional teams."

Poliakine also affirms that no human force available today that can support this volume with the necessary speed and efficiency. Time and again, this is a point made forcefully by other individuals and companies working in this burgeoning sector.

"When it comes to detecting outbreaks, machines can be trained to process vast amounts of data in the same way that a human expert would," explains Dr Kamran Khan, the founder and CEO of BlueDot, as well as a professor at the University of Toronto. "But a machine can do this around the clock, tirelessly, and with incredible speed, making the process vastly more scalable, timely, and efficient. This complements human intelligence to interpret the data, assess its relevance, and consider how best to apply it with decision-making."

Basically, AI is set to become a giant firewall against infectious diseases and pandemics. And it won't only be because of AI-assisted screening and diagnostic techniques. Because as Sergey Young, a longevity expert and founder of the Longevity Vision Fund, tells me, artificial intelligence will also be pivotal in identifying potential vaccines and treatments against the next coronavirus, as well as COVID-19 itself.

"AI has the capacity to quickly search enormous databases for an existing drug that can fight coronavirus or develop a new one in literally months," he says. "For example, Longevity Vision Funds portfolio company Insilico Medicine, which specializes in AI in the area of drug discovery and development, used its AI-based system to identify thousands of new molecules that could serve as potential medications for coronavirus in just four days. The speed and scalability of AI is essential to fast-tracking drug trials and the development of vaccines."

This kind of treatment-discovery will prove vitally important in the future. And in conjunction with screening, it suggests that artificial intelligence will become one of the primary ingredients in ensuring that another coronavirus won't have an outsized impact on the global economy. Already, the COVID-19 coronavirus is likely to cut global GDP growth by $1.1 trillion this year, in addition to having already wiped around $5 trillion off the value of global stock markets. Clearly, avoiding such financial destruction in the future would be more than welcome, and artificial intelligence will prove indispensable in this respect. Especially as the scale of potential pandemics increases with an increasingly populated and globalised world.

Sergey Young also explains that AI could play a substantial role in the area of impact management and treatment, at least if we accept their increasing encroachment into society. He notes that, in China, robots are being used in hospitals to alleviate the stresses currently being piled on medical staff, while ambulances in the city of Hangzhou are assisted by navigational AI to help them reach patients faster. Robots have even been dispatched to a public plaza in Guangzhou in order to warn passersby who aren't wearing face-masks. Even more dystopian, China is also allegedly using drones to ensure residents are staying at home and reducing the risk of the coronavirus spreading further.

Even if we don't reach that strange point in human history where AI and robots police our behaviour during possible health crises, artificial intelligence will still become massively important in detecting outbreaks before they spread and in identifying possible treatments. Companies such as BlueDot, Nanox, and Insilico Medicine will prove increasingly essential in warding off future coronavirus-style pandemics, and with it they'll provide one very strong example of AI being a force for good.

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How AI May Prevent The Next Coronavirus Outbreak - Forbes

Artificial Intelligence has progressed immensely in the past few years. From being just a fiction context to penetrating into the regular lives of people, AI has brought transformation in several ways. Such advancements are an output of various factors that include the application of new statistical approaches and enhanced computing powers. However, according to 2017 report by DeepMind,a Perspective in the journal Neuron, argues that people often discount the contribution and use of ideas from experimental and theoretical neuroscience.

TheDeepMind reportsresearchers believe that drawing inspiration from neuroscience in AI research is important for two reasons. First, neuroscience can help validate AI techniques that already exist. They said, Put simply if we discover one of our artificial algorithms mimics a function within the brain, it suggests our approach may be on the right track. Second, neuroscience can provide a rich source of inspiration for new types of algorithms and architectures to employ when building artificial brains. Traditional approaches to AI have historically been dominated by logic-based methods and theoretical mathematical models.

Moreover,in a recent blog post, DeepMind suggests that the human brain and AI learning methods are closely linked when it comes to learning through reward.

Computer scientists have developed algorithms for reinforcement learning in artificial systems. These algorithms enable AI systems to learn complex strategies without external instruction, guided instead by reward predictions.

As noted by the post, a recent development in computer science which yields significant improvements in performance on reinforcement learning problems may provide a deep, parsimonious explanation for several previously unexplained features of reward learning in the brain, and opens up new avenues of research into the brains dopamine system, with potential implications for learning and motivation disorders.

DeepMind found that dopamine neurons in the brain were each tuned to different levels of pessimism or optimism. If they were a choir, they wouldnt all be singing the same note, but harmonizing each with a consistent vocal register, like bass and soprano singers. In artificial reinforcement learning systems, this diverse tuning creates a richer training signal that greatly speeds learning in neural networks, and researchers speculate that the brain might use it for the same reason.

The existence of distributional reinforcement learning in the brain has interesting implications both for AI and neuroscience. Firstly, this discovery validates distributional reinforcement learning it gives researchers increased confidence that AI research is on the right track since this algorithm is already being used in the most intelligent entity they are aware of: the brain.

Therefore, a shared framework for intelligence in context to artificial intelligence and neuroscience will allow scientists to build smarter machines, and enable them to understand humankind better. This collaborative drive to propel both could possibly expand human cognitive capabilities while bridging the gap between humans and machines.

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Smriti is a Content Analyst at Analytics Insight. She writes Tech/Business articles for Analytics Insight. Her creative work can be confirmed @analyticsinsight.net. She adores crushing over books, crafts, creative works and people, movies and music from eternity!!

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How AI and Neuroscience Can Help Each Other Progress? - Analytics Insight

Does the law of war apply to artificially intelligent systems? The U.S. Department of Defense is taking this question seriously, and adopted ethical principles for artificial intelligence (AI) in February 2020 based on the Defense Innovation Boards set of AI ethical guidelines proposed last year. However, just as defense organizations must abide by the law of war and other norms and values, individuals are responsible for the systems they create and use.

Ensuring that AI systems are as committed as we are to responsible and lawful behavior requires changes to engineering practices. Considerations of ethical, moral, and legal implications are not new to defense organizations, but they are starting to become more common in AI engineering teams. AI systems are revolutionizing many commercial sector products and services, and are applicable to many military applications from institutional processes and logistics to those informing warfighters in the field. As AI becomes ubiquitous, changes are necessary to integrate ethics into AI development now, before it is too late.

The United States needs a curious, ethical AI workforce working collaboratively to make trustworthy AI systems. Members of AI development teams must have deep discussions regarding the implications of their work on the warfighters using them. This work does not come easily. In order to develop AI systems effectively and ethically, defense organizations should foster an ethical, inclusive work environment and hire a diverse workforce. This workforce should include curiosity experts (people who focus on human needs and behaviors), who are more likely to imagine the potential unwanted and unintended consequences associated with the systems use and misuse, and ask tough questions about those consequences.

Create an Ethical, Inclusive Environment

People with similar concepts of the world and a similar education are more likely to miss the same issues due to their shared bias. The data used by AI systems are similarly biased, and people collecting the data may not be aware of how that is conveyed through the data they create. An organizations bias will be pervasive in the data provided by that organization, and the AI systems developed with that data will perpetuate the bias.

Bias can be mitigated with ethical workforces that value diverse human intelligence and the wide set of possible life experiences. Diversity doesnt just mean making sure that there are a mix of genders on the project team, or that people look different, though those attributes are important. A project team should have a wide set of life experiences, disability status, social status, and experience being the other. Diversity also means including a mix of people in uniform, civilians, academic partners, and contractors as well as those individuals who have diverse life experiences. This diversity does not mean lowering the bar of experience or talent, but rather extending it. To be successful, all of these individuals need to be engaged as full members of the project team in an inclusive environment.

Individuals coming from different backgrounds will be more capable of imagining a broad set of uses, and more importantly, misuses of these systems. Assembling a diverse workforce that brings talented, experienced people together will reinforce technology ethics. Imbuing the workforce with curiosity, empathy, and understanding for the warfighters using the systems and affected by the systems will further support the work.

Diverse and inclusive leadership is key to an organizations success. When leadership in the organization isnt diverse, it is less likely to attract and, more importantly, retain talent. This is primarily thought to be because those talented individuals may assume that the organization is not inclusive or that there is no future in the organization for them. If leadership is lacking in diversity, an organization can promote someone early or hire from the outside if necessary.

Adopting a set of technology ethics is a first step to supporting project teams in making better, more confident decisions that are ethical. Technology ethics are ethics that are designed specifically for development of software and emerging technologies. They help align diverse project teams to assist them in setting appropriate norms for AI systems. Much like physicians adhere to a version of the American Medical Associations Code of Medical Ethics, technology ethics help guide a project team working on AI systems that have the potential for harm (most AI systems do). These project teams need to have early, difficult conversations about how they will manage a variety of situations.

A shared set of technology ethics serves as a central point to guide decision-making. We are all unique, and many of us have shared knowledge and experiences. These are what naturally draw people together, making it feel like a bigger challenge to work with people who have completely different experiences. However, the experience of working with people who are significantly different builds the capacity for innovation and creative thinking. Using ethics as a bridge between differences strengthens the team by creating shared knowledge and common ground. Technology ethics must be weaved into the work at a very early stage, and the AI workforce must continue to advocate technology ethics as the AI system matures. Human involvement (a human-in-the-loop) is required throughout the life cycle of AI systems an AI system cannot be simply turned on and left to run. Technology ethics should be considered throughout the entire life cycle.

Without technology ethics it is harder for project teams to align, and important discussions may be inadvertently skipped. Technology ethics bring into focus the obligation for the project team to take its work and its implications seriously, and can also empower individuals to ask tough questions with regard to unwanted and unintended consequences that they imagine with the systems use and misuse. By aligning on a set of technology ethics, the development team can define clear directives with regard to system functionality.

Identifying a set of technology ethics is an intimidating task and one that should be approached carefully. Some project teams will want to adopt guidance initially from organizations such as the Association for Computing Machinerys Code of Ethics and Professional Conduct, and the Montreal Declaration for a Responsible Development of Artificial Intelligence, while others like IBM and Microsoft are developing their own guidance. The Defense Departments newly adopted five AI ethics principles are: responsible, equitable, traceable, reliable, and governable. The original Defense Innovation Board recommendation is described in detail in the supporting document.

In the past, ethics have only been referenced in, and not directly part of, software development efforts. The knowledge that AI systems can cause much broader harm more quickly than software technologies could in the past raises new ethical questions that need to be addressed by the AI workforce. A skilled and diverse workforce, bursting with curiosity and engaged with the AI system, will result in AI systems that are accountable to humans, de-risked, respectful, secure, honest, and usable.

Value Curiosity

AI systems will be created and used by a wide range of individuals, and misuse will come from potentially unexpected sources individuals and organizations with completely different experiences and with potentially unlimited resources. Adversaries already are using techniques that are very difficult to anticipate. The adoption of technology ethics isnt enough to make AI systems safe. Making sure that the teams building these systems are able to imagine and then mitigate issues is profoundly important.

The term curiosity experts is shorthand for people who have a broad range of skills and job titles including: cognitive psychologists, digital anthropologists, human-machine interaction and human-computer interaction professionals, and user experience researchers and designers. Curiosity experts core responsibility is to be curious and speculative within the ethical and inclusive environment an organization has created. Curiosity experts will partner with defense experts, and may already be part of your team, doing research and helping to make interactions more usable.

Curiosity experts help connect the human needs, the initial problem to be solved, and the solution to an engineering problem. Working with defense experts (and ideally the warfighters themselves), they will enable a project team to uncover potential issues before they arise by focusing on understanding how the system will be used, the situation and constraints for using the system, as well as the abilities of the people who will use it. Curiosity experts can conduct a variety of proven qualitative and quantitative methods, and once they have a solid understanding, they share that information with the project team in easy to consume formats such as stories. The research they conduct is necessary to understand the needs that are being addressed, so that the team builds the right thing. This may sound familiar wargaming uses very similar tactics, and storytelling is an important component.

Its important for curiosity experts to lead (and then teach others to lead) co-design activities such as abusability testing and other speculative exercises, in which the project team imagines the misuse of the AI system they are considering building. AI systems need to be interpretable and usable by warfighters, and this has been recognized as a priority by the Defense Advanced Research Projects Agency, which is working on the Explainable AI program. Curiosity experts with interaction design experience can contribute materially to this effort as they help keep the people using these systems in mind, and call out the AI workforce when necessary. When the project team asks, Why dont they get it? curiosity experts can nudge the project team to pivot instead to What can we do better to meet the warfighters needs? As individuals on the team become more comfortable with this mindset, they become curiosity experts at heart, even when their primary responsibility is something else.

Hire a Diverse Workforce

Building diverse project teams helps to increase each individuals creativity and effectiveness. Diversity in this sense relates to skill sets, education (with regard to school and program), and problem-framing approach. Coming together with different ways of looking at the world will help teams and organizations solve challenging problems faster.

Building a diverse project team to advance this ethical framework will take time and effort. Organizations that represent minority groups such as the National Society of Black Engineers and technical conferences that embrace diversity such as the Grace Hopper Celebration can be a great resource. Prospective candidates should ask hard questions about the organization, including about the organizations ethics, diversity, and inclusion. These questions are indicative of curious individuals you want on your team. Once you recruit more diverse individuals, you can set progress goals. For example, Atlassian introduced a new approach to diversity reporting in 2016 that focused on team dynamics and shared how people from underrepresented backgrounds were spread across the companys teams.

It is common in technology, and AI specifically, to value specific degrees and learning styles. Some employers have staffed their organization with class after class of graduates from particular degree programs at particular universities. These organizations benefit from the ability of these graduates to easily bond and rely on shared knowledge. However, these same benefits can become weaknesses for the project team. The peril of creating high-risk products and services with a homogeneous team is that they may all miss the same critical piece of information; have the same gaps in technical knowledge; assume the same things about the process; or not be able to think differently enough to imagine unintended consequences. They wont even realize their mistake until it is too late.

In many organizations this risk is disguised by adding one or two individuals to a group who are significantly different from the majority in an aspect such as gender, race, or culture. Unfortunately, their presence isnt enough to significantly reduce the risk of groupthink, and their experience will be dismissed because it is different if there are not enough individuals who are socially distinct in the group. Eventually, due to many of these factors, retention becomes a significant concern. Project teams need to be built with diversity from the start, or be quickly adjusted.

A diverse team of thoughtful and talented machine learning experts, programmers, and curiosity experts (among others) is not yet complete. The AI workforce needs direct access to experts in the military or defense industry who are familiar with the situations and organizations the AI system is being designed for, and who can spot assumptions and issues early. These individuals, be they in uniform, civilians, or consultants, may also be able to act as liaisons to the warfighters so that more direct contact can be made with those closest to the work.

Rethinking the Workforce

Encouraging project teams to be curious and speculative in imagining scenarios at the edges of AI will help to prepare for actual system use. As the AI workforce considers how to manage a variety of use cases, framing conversations with technology ethics will provoke serious and contentious discussions. These conversations are precious with regard to aligning the team prior to facing a difficult situation. A clear understanding of what the expectations are in specific situations helps the team to create mitigation plans for how they will respond, both during the creation of the AI system and once it is in production.

The AI sector needs to think about the workforce in different ways. As Prof. Hannah Fry suggests in The Guardian, diversity and inclusion in the workforce is just as important as a technology ethics pledge (if not more so) to ensure that we are reducing unwanted bias and unintended consequences. Creating an ethical, inclusive environment, valuing curiosity, and hiring a diverse workforce are necessary steps to make ethical AI. Clear communication and alignment on ethics is the best way to bring disparate groups of people into a shared understanding and to create AI systems that are accountable to humans, de-risked, respectful, secure, honest, and usable.

Over the next several years, my organization, Carnegie Mellon Universitys Software Engineering Institute, is advancing a professional discipline of AI Engineering to help the defense and national security communities develop, deploy, operate, and evolve game-changing mission capabilities that leverage rapidly evolving artificial intelligence and machine learning technologies. At the core of this effort is supporting the AI workforce in designing trustworthy AI systems by successfully integrating ethics in a diverse workforce.

Carol Smith (@carologic) is a senior research scientist in Human-Machine Interaction at Carnegie Mellon Universitys Software Engineering Institute and an adjunct instructor for CMUs Human-Computer Interaction Institute. She has been conducting user experience research to improve the human experience across industries for 19 years and working to improve AI systems since 2015. Carol is recognized globally as a leader in user experience and has presented over 140 talks and workshops in over 40 cities around the world, served two terms on the User Experience Professionals Association international board, and is currently an editor for the Journal of Usability Studies and the upcoming Association for Computing Machinery Digital Threats: Research and Practice journals Special Issue on Human-Machine Teaming. She holds an M.S. in Human-Computer Interaction from DePaul University.

This material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center.

The views, opinions, and/or findings contained in this material are those of the author(s) and should not be construed as an official government position, policy, or decision, unless designated by other documentation.

Image: U.S. Air Force (Photo by J.M. Eddins Jr.)

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Creating a Curious, Ethical, and Diverse AI Workforce - War on the Rocks

Are artificial intelligence (AI) and superintelligent machines the best or worst thing that could ever happen to humankind? This has been a question in existence since the 1940s when computer scientist Alan Turing wondered and began to believe that there would be a time when machines could have an unlimited impact on humanity through a process that mimicked evolution.

Is Artificial Intelligence (AI) A Threat To Humans?

When Oxford University Professor Nick Bostroms New York Times best-seller, Superintelligence: Paths, Dangers, Strategies was first published in 2014, it struck a nerve at the heart of this debate with its focus on all the things that could go wrong. However, in my recent conversation with Bostrom, he also acknowledged theres an enormous upside to artificial intelligence technology.

You can see the full video of our conversation here:

Since the writing of Bostrom's book in 2014, progress has been very rapid in artificial intelligence and machine and deep learning. Artificial intelligence is in the public discourse, and most governments have some sort of strategy or road map to address AI. In his book, he talked about AI being a little bit like children playing with a bomb that could go off at any time.

Bostrom explained, "There's a mismatch between our level of maturity in terms of our wisdom, our ability to cooperate as a species on the one hand and on the other hand our instrumental ability to use technology to make big changes in the world. It seems like we've grown stronger faster than we've grown wiser."

There are all kinds of exciting AI tools and applications that are beginning to affect the economy in many ways. These shouldnt be overshadowed by the overhype on the hypothetical future point where you get AIs with the same general learning and planning abilities that humans have as well as superintelligent machines.These are two different contexts that require attention.

Today, the more imminent threat isn't from a superintelligence, but the usefulyet potentially dangerousapplications AI is used for presently.

How is AI dangerous?

If we focus on whats possible today with AI, here are some of the potential negative impacts of artificial intelligence that we should consider and plan for:

Change the jobs humans do/job automation: AI will change the workplace and the jobs that humans do. Some jobs will be lost to AI technology, so humans will need to embrace the change and find new activities that will provide them the social and mental benefits their job provided.

Political, legal, and social ramifications: As Bostrom advises, rather than avoid pursuing AI innovation, "Our focus should be on putting ourselves in the best possible position so that when all the pieces fall into place, we've done our homework. We've developed scalable AI control methods, we've thought hard about the ethics and the governments, etc. And then proceed further and then hopefully have an extremely good outcome from that." If our governments and business institutions don't spend time now formulating rules, regulations, and responsibilities, there could be significant negative ramifications as AI continues to mature.

AI-enabled terrorism: Artificial intelligence will change the way conflicts are fought from autonomous drones, robotic swarms, and remote and nanorobot attacks. In addition to being concerned with a nuclear arms race, we'll need to monitor the global autonomous weapons race.

Social manipulation and AI bias: So far, AI is still at risk for being biased by the humans that build it. If there is bias in the data sets the AI is trained from, that bias will affect AI action. In the wrong hands, AI can be used, as it was in the 2016 U.S. presidential election, for social manipulation and to amplify misinformation.

AI surveillance: AIs face recognition capabilities give us conveniences such as being able to unlock phones and gain access to a building without keys, but it also launched what many civil liberties groups believe is alarming surveillance of the public. In China and other countries, the police and government are invading public privacy by using face recognition technology. Bostrom explains that AI's ability to monitor the global information systems from surveillance data, cameras, and mining social network communication has great potential for good and for bad.

Deepfakes: AI technology makes it very easy to create "fake" videos of real people. These can be used without an individual's permission to spread fake news, create porn in a person's likeness who actually isn't acting in it, and more to not only damage an individual's reputation but livelihood. The technology is getting so good the possibility for people to be duped by it is high.

As Nick Bostrom explained, The biggest threat is the longer-term problem introducing something radical thats super intelligent and failing to align it with human values and intentions. This is a big technical problem. Wed succeed at solving the capability problem before we succeed at solving the safety and alignment problem.

Today, Nick describes himself as a frightful optimist that is very excited about what AI can do if we get it right. He said, The near-term effects are just overwhelmingly positive. The longer-term effect is more of an open question and is very hard to predict. If we do our homework and the more we get our act together as a world and a species in whatever time we have available, the better we are prepared for this, the better the odds for a favorable outcome. In that case, it could be extremely favorable.

For more on AI and other technology trends, see Bernard Marrs new book Tech Trends in Practice: The 25 Technologies That Are Driving The 4Th Industrial Revolution, which is available to pre-order now.

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Is Artificial Intelligence (AI) A Threat To Humans? - Forbes

The Joint Artificial Intelligence Center will take a Series A, B, approach to building tech for customers, with product managers and mission teams.

The Joint Artificial Intelligence Center will take a Series A, B, approach to building tech for customers, with product managers and mission teams. By PatrickTucker

Military leaders who long to copy the way Silicon Valley funds projects should know: the Valley isnt the hit machine people think it is, says Nand Mulchandani, chief technical officer of the Pentagons Joint Artificial Intelligence Center. The key is to follow the right venture capitalmodel.

Mulchandani, a veteran of several successful startups, aims to ensure JAICs investments in AI software and tools actually work out. So he is bringing a very specific venture-capital approach to thePentagon.

Heres the plan: when a DoD agency or military branch asks JAIC for help with some mission or activity, the Center will assign a mission team of, essentially, customer representatives to figure out what agency data might be relevant to theproblem.

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Next, the JAIC will assign a product manager not DoDs customary program manager, but a role imported from the techindustry.

He or she handles the actual building of the product, not the administrative logistics of running a program. The product manager will gather customer needs, make those into product features, work with the program manager, ask, What does the product do? How is it priced? Mulchandani told Defense One in a phone conversation onThursday.

The mission team and product manager will take a small part of the agencys data to the software vendors or programs that they hire to solve the problem. These vendors will need to prove their solution works before scaling up to take on all availabledata.

Were going to have a Series A, a seed amount of money. You [the vendor] get a half a million bucks to curate the data, which tends to be the problem. Do the problem x in a very tiny way, taking sample data, seeing if an algorithm applies to it, and then scale it, Mulchandani saidon Wednesday at an event hosted by the Intelligence and National Security Alliance, orINSA.

In the venture capital industry, you take a large project, identify core risk factors, like team risk, customer risk, etc. you fund enough to take care of these risks and see if you can overcome the risks through a prototype or simulation, before you try to scale, he addedlater.

The customer must also plan to turn the product into a program of record or give it some other life outside of theJAIC.

Thats very different from the way the Defense Department pays for tech today, he said. The unit of currency in the DoD seems to be Well, this was a great idea; lets stick a couple million bucks on it, see what happens. Were not doing that way anymore he said onWednesday.

The JAIC is working with the General Services Administration Centers of Excellence to create product manager roles in DoD and to figure out how to scale small solutions up. Recently, some members of the JAIC and the Centers of Excellence participated in a series of human-centered design workshops to determine essential roles and responsibilities for managing data assets, across areas that the JAIC will be developing products, like cybersecurity, healthcare, predictive maintenance, and business automation, according to thestatement.

Mulchandani urges the Pentagon not to make a fetish of Silicon Valley. Without the right business and funding processes, many venture startups fail just as badly as poorly thought out government projects. You just dont hear aboutthem.

When you end up in a situation where theres too much capital chasing too few good ideas that are real, you end up in a situation where you are funding a lot of junk. What ends up happening [in Silicon Valley] is many of those companies just fail, he said Wednesday. The problem in DOD is similar. How do you apply discipline up front, on a venture model, to fund the good stuff as opposed to funding a lot of junk and then seeing two or three products that becomesuccessful?

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The Pentagon's AI Shop Takes A Venture Capital Approach to Funding Tech - Defense One