Category Archives: Artificial Intelligence
Will Artificial Intelligence Ever Live Up to Its Hype? – Scientific American
When I started writing about science decades ago, artificial intelligence seemed ascendant. IEEE Spectrum, the technology magazine for which I worked, produced a special issue on how AI would transform the world. I edited an article in which computer scientist Frederick Hayes-Roth predicted that AI would soon replace experts in law, medicine, finance and other professions.
That was in 1984. Not long afterward, the exuberance gave way to a slump known as an AI winter, when disillusionment set in and funding declined. Years later, doing research for my book The Undiscovered Mind, I tracked Hayes-Roth down to ask how he thought his predictions had held up. He laughed and replied, Youve got a mean streak.
AI had not lived up to expectations, he acknowledged. Our minds are hard to replicate, because we are very, very complicated systems that are both evolved and adapted through learning to deal well and differentially with dozens of variables at one time. Algorithms that can perform a specialized task, like playing chess, cannot be easily adapted for other purposes. It is an example of what is called nonrecurrent engineering, Hayes-Roth explained.
That was 1998. Today, according to some measures, AI is booming once again. Programs such as voice and face recognition are embedded in cell phones, televisions, cars and countless other consumer products. Clever algorithms help me choose a Christmas present for my girlfriend, find my daughters building in Brooklyn and gather information for columns like this one. Venture-capital investments in AI doubled between 2017 and 2018 to $40 billion,according to WIRED. A Price Waterhouse study estimates that by 2030 AI will boost global economic output by more than $15 trillion, more than the current output of China and India combined.
In fact, some observers fear that AI is moving too fast. New York Times columnist Farhad Manjoo calls an AI-based reading and writing program, GPT-3, amazing, spooky, humbling and more than a little terrifying. Someday, he frets, he might be put out to pasture by a machine. Neuroscientist Christof Koch has suggested that we might need computer chips implanted in our brains to help us keep up with intelligent machines.
Elon Musk made headlines in 2018 when he warned that superintelligent AI, much smarter than we are, represents the single biggest existential crisis that we face. (Really? Worse than climate change? Nuclear weapons? Psychopathic politicians? I suspect that Musk, whohas invested in AI, is trying to promote the technology with his over-the-top fearmongering.)
Experts are pushing back against the hype, pointing out that many alleged advances in AI are based on flimsy evidence. Last January, for example, a team from Google Health claimed in Nature that their AI program had outperformed humans in diagnosing breast cancer. In October, a group led by Benjamin Haibe-Kains, a computational genomics researcher, criticized the Google health paper, arguing that the lack of details of the methods and algorithm code undermines its scientific value.
Haibe-Kains complained to Technology Review that the Google Health report is more an advertisement for cool technology than a legitimate, reproducible scientific study. The same is true of other reported advances, he said. Indeed, artificial intelligence, like biomedicine and other fields, has become mired in a replication crisis. Researchers make dramatic claims that cannot be tested, because researchersespecially those in industrydo not disclose their algorithms. One recent review found that only 15 percent of AI studies shared their code.
There are also signs that investments in AI are not paying off. Technology analyst Jeffrey Funk recently examined 40 start-up companies developing AI for health care, manufacturing, energy, finance, cybersecurity, transportation and other industries. Many of them were not nearly as valuable to society as all the hype would suggest, Funk reports in IEEE Spectrum. Advances in AI are unlikely to be nearly as disruptivefor companies, for workers, or for the economy as a wholeas many observers have been arguing.
Science reports that core progress in AI has stalled in some fields, such as information retrieval and product recommendation. A study of algorithms used to improve the performance of neural networks found no clear evidence of performance improvements over a 10-year period.
The longstanding goal of general artificial intelligence, possessing the broad knowledge and learning capacity to solve a variety of real-world problems, as humans do, remains elusive. We have machines that learn in a very narrow way, Yoshua Bengio, a pioneer in the AI approach called deep learning, recently complained in WIRED. They need much more data to learn a task than human examples of intelligence, and they still make stupid mistakes.
Writing in The Gradient, an online magazine devoted to tech, AI entrepreneur and writer Gary Marcus accuses AI leaders as well as the media of exaggerating the fields progress. AI-based autonomous cars, fake news detectors, diagnostic programs and chatbots have all been oversold, Marcus contends. He warns that if and when the public, governments, and investment community recognize that they have been sold an unrealistic picture of AIs strengths and weaknesses that doesn't match reality, a new AI winter may commence.
Another AI veteran and writer, Erik Larson, questions the myth that one day AI will inevitably equal or surpass human intelligence. In The Myth of Artificial Intelligence: Why Computers Cant Think the Way We Do, scheduled to be released by Harvard University Press in April, Larson argues that success with narrow applications gets us not one step closer to general intelligence.
Larson says the actual science of AI (as opposed to the pseudoscience of Hollywood and science fiction novelists) has uncovered a very large mystery at the heart of intelligence, which no one currently has a clue how to solve. Put bluntly: all evidence suggests that human and machine intelligence are radically different. And yet the myth of inevitability persists.
When I first started writing about science, I believed the myth of AI. One day, surely, researchers would achieve the goal of a flexible, supersmart, all-purpose artificial intelligence, like HAL. Given rapid advances in computer hardware and software, it was only a matter of time. And who was I to doubt authorities like Marvin Minsky?
Gradually, I became an AI doubter, as I realized that our mindsin spite of enormous advances in neuroscience, genetics, cognitive science and, yes, artificial intelligenceremain as mysterious as ever. Heres the paradox: machines are becoming undeniably smarterand humans, it seems lately, more stupid, and yet machines will never equal, let alone surpass, our intelligence. They will always remain mere machines. Thats my guess, and my hope.
Further Reading:
How Would AI Cover an AI Conference?
Do We Need Brain Implants to Keep Up with Robots?
The Many Minds of Marvin Minsky (R.I.P.)
The Singularity and the Neural Code
Who Wants to Be a Cyborg?
Mind-Body Problems
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Will Artificial Intelligence Ever Live Up to Its Hype? - Scientific American
Governments And Artificial Intelligence, Policy And Investment – Forbes
Artificial Intelligence
Over the last couple of years, it has become increasingly clear that many democratic governments have been taking a closer look at artificial intelligence (AI), both from a policy standpoint and as something to help their economies of the future. I specify democratic because of two reasons. First, its clear that China recognized both the economic power and the population control capabilities of AI much earlier. Democracies have many open issues and can move more slowly, and policy is discussed more widely by the population. Two pieces of news this week have shown the increasing focus on AI in the United States and the European Union (EU).
The Brookings Institution has increased its focus on AI, even publishing an interesting book I reviewed earlier this year. On December 1, Brookings held a webinar on the future of tech antitrust in the Biden administration. While the subject doesnt directly relate to AI, there are clear implications. Two of the first companies to operationalize AI were Amazon and Google, both targets for potential antitrust action. To understand how that could impact the industry, many people are talking about the failed actions against Microsoft a few decades ago. I go further back. People tend to forget that IBM was broken up because it controlled the software and the hardware in the mainframe days. Splitting the groups is what allowed the creation of the independent software vendor (ISV), which is todays software industry.
Artificial intelligence is going to have a massive impact on our lives. Right now there are many small players, but acquisitions will happen as that has always been a regular exit strategy. The question is when acquisitions reach a level of restraint of trade or monopolistic anti-competition. One of the key points in the webinar was that technology is being recognized as different. This isnt the days of Shell Oil. The internet is wider, and the big five of Amazon, Google, Facebook, Apple and Microsoft can all have potentially monopolistic practices in one area while being behind the others in different areas. Understanding the modern business landscape is going to be difficult and will take time.
While the webinar came to the same conclusion, I had going it, that it was too early to make any realistic predictions about what the new Biden administration will do, it is clear that there will be a fresh look taken at antitrust and that will have a potential impact on AI.
On the other side of the pond, the EU continues to increase its focus on AI as a future economic driver. A press release announced yet another AI investment initiative. Two EU groups, the European Investment Bank Group (EIB Group) stated that it has created an instrument to co-invest up to 150 million alongside the European Investment Fund (EIF) in AI, blockchain, IoT and related advanced technologies.
That investment isnt the first and wont be the last. The EU will continue to increase its support of new technologies, and thats a good thing. The US is still drifting a bit but, going back to the Biden administration, I expect an increasing level of funding to help younger companies. Yes, there is already private investment, but governments help focus on issue key to a growing economy and need to be involved. First, because that helps drive technology innovations that are more focused on expected needs. Second, because sometimes driving new technology has wider impact than expected, as how the DARPA investment in creating a network to reach a few universities drove the creation of the infrastructure that drives how youre now reading my writing.
Third, lets circle back to the first paragraph. Non-democracies know the power of technology to help their economies, to compete in international politics, and to control their people so the leaders retain power. China is heavily investing in AI. Based on the last four years of international politics, its also gained an advantage in spreading its business and political footprint into emerging markets. It is critical that the EU, the US, and other nations work on technologies that can help support, improve and spread the types of societies and economies that our own societies feel present the best for both citizens and business. Artificial intelligence is going to have enormous impact on the world, and governments will be competing to define and control that impact.
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Governments And Artificial Intelligence, Policy And Investment - Forbes
Artificial Intelligence Research Catalyst Fund awards 20 grants totaling $1 million – University of Florida
The University of Florida has awarded 20 faculty teams $50,000 each from UF Research's Artificial Intelligence Research Catalyst Fund to pursue a wide range of AI-related projects. The researchers will utilize the universitys world-leading computing capabilities to analyze vast amounts of data and predict solutions to health, agriculture, engineering and educational challenges.
A team of faculty reviewers evaluated 133 proposals from across the university before settling on 20 that the group determined had the most potential for elevating UFs AI research profile. The projects will leverage the universitys new computing capabilities, which are being developed through $60 million in gifts from alumnus Chris Malachowsky and NVIDIA, the company he co-founded.
As part of UFs push to become a national leader in artificial intelligence research and education, the AI Research Catalyst Fund was created to encourage multidisciplinary teams of faculty and students to rapidly pursue imaginative applications of AI across the institution, said David Norton, UFs vice president for research. We anticipate that this seed funding will position these teams to pursue additional funding from government agencies and industry.
Among the projects are one to use AI to identify biomarkers that will facilitate the early detection of Alzheimers Disease.
By 2030, over 100 million people are expected to be living with Alzheimers Disease, said Juan Claudio Nino, a professor of materials science and engineering, and principal investigator on the project. Development of quantitative brain biomarkers to help early-stage detection, diagnostic precision, and guide intervention in Alzheimers is essential.
Nino is working with Marcelo Febo, an associate professor in psychiatry and neuroscience, to use AI to analyze functional magnetic resonance images of healthy and diseased brains to identify clues to the onset of the disease.
Emre Tepe, an assistant professor of urban and regional planning, and Abolfazl Safikhani, assistant professor of statistics, will be using machine learning to track past and present land use patterns in Florida to simulate future impacts of anticipated changes in land developments.
This model can also be easily applied to a wide spectrum of research areas such as traffic forecasting, urban energy consumption and disease spread, Tepe said.
Another team will use machine learning to mine a large dataset of student responses to math problems on UFs Algebra Nation platform to help teachers identify academically at-risk students even if they are learning remotely.
We hope to use machine learning techniques to analyze big data to automatically detect students emotions and engagement factors, two of the most important factors influencing online students learning outcomes, said Wanli Xing, an assistant professor in the College of Educations School of Teaching and Learning.
The team will also be looking for ways to ensure that AI tools used to identify at-risk students do not unfairly reinforce gender, race and other inequalities.
For example, a student course grade prediction model is twice as likely to incorrectly predict African-American students as having a high risk of failure compared to their Caucasian counterparts, said G. Bahar Basim, a professor of materials science and engineering and co-principal investigator with Xing. This can result in over-intervention and other undesirable consequences for African-American students.
Another project led by Alina Zare, a professor of electrical and computer engineering, and Peter DiGennaro, assistant professor of entomology and nematology, will pair UFs large dataset of nematode images with Zares researchon automated plant root analysis tocreate a new way of quickly identify the agricultural pests in the soil.
Effective management requires accurate parasitic nematode identification,DiGennarosaid, but human-based identification requires years of intensive training. Developing a machine learning algorithm to identify and quantify nematode species could revolutionize parasitic nematode identification services, increasing speed and accuracy of recommendations to farmers.
Artificial intelligence is accelerating our ability to develop solutions to complex problems previously viewed as intractable, Norton said. With these investments in our researchers, we are accelerating UFs impact in areas that benefit our state, our nation and the planet.
Joe Kays December 4, 2020
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Artificial Intelligence Research Catalyst Fund awards 20 grants totaling $1 million - University of Florida
Artificial Intelligence in Government and the Presidential Transition: Building on a Solid Foundation – Nextgov
Artificial intelligence allows computerized systems to perform tasks traditionally requiring human intelligence: analytics, decision support, visual perception and foreign language translation. AI and robotics process automation, or RPA, have the potential to spur economic growth, enhance national security, and improve the quality of life. In a world of Big Data and Thick Data, AI tools can process huge amounts of data in seconds, automating tasks that would take days or longer for human beings to performand the public sector in the United States is at the very beginning of a long-term journey to develop and harness these tools.
The National Academy of Public Administration identified Making Government AI Ready as one of the Grand Challenges in Public Administration. I chaired the Academys Election 2020 Project Working Group on AI. Our reportreleased in August 2020contained a series of practical nonpartisan recommendations for how the administration in 2021 should address this Grand Challenge.
Clearly, our nation is deeply divided, and many citizens are dismissive of science and technology. If citizens dont trust one another, might there be a day when they trust machines more? What will the promises of AI bring and why is this important? And given a large portion of todays society seeming inability to accept facts, can AI one day be used to curtail the spread of conspiracy theories?
Despite the divisiveness of todays political landscape, it is reassuring to note that a cadre of highly dedicated and knowledgeable career public managers have traditionally passed the torch of technology innovation from one administration to another. I expect that this will happen over the next couple of months even amidst current political turmoil.
What are key steps that the incoming Biden administration should take to make the federal government AI-ready? First, it can build upon the progress made on AI during the Trump administration. Of particular importance was the AI executive order issued in February 2019. This order directed the federal government to pursue five goals: invest in AI research and development, unleash AI resources, remove barriers to AI innovation, train an AI-ready workforce, and promote an international environment supportive of American AI innovation and responsible use. Federal agencies were also directed to identify ways that they can enable the use of cloud computing for AI R&D.
Other recommendations include:
It is especially critical for the incoming administration to build a trustworthy AI environment. With a skeptical public, a majority of Americans recognize the need to carefully manage AI, with the greatest importance placed on safeguarding data privacy; protecting against AI-enhanced cyberattacks, surveillance, and data manipulation; and ensuring the safety of autonomous vehicles, accuracy and transparency of disease diagnosis, and the alignment of AI with human values.
And building trust will require an ethical framework. Today we recognize that AI when coupled with huge amounts of (quality) data can be highly useful in identifying patterns, seeking out anomalies, making real-time recommendations based on data inputs, communicating both verbally and in writing, and all the time learning and perfecting. But what happens if the quality of data is found to be flawed and what if it is found that there may be unintended bias in the increasingly complex algorithms?
Implementing these recommendations will require a sustained leadership commitment and steadfast focus, sufficient funding, and both interagency and intergovernmental coordination. I have every reason to believe the great work that started in 2019 will continue its journey for many years to come. And if anyone is seeking a solid example of what AI can do, the current string of breakthrough COVID-19 vaccine announcements was made possible by applying AI towards analyzing the DNA of the virus itself. As a result of massive simulation with combinations and known interactions, a promising cure came about in a mere six months instead of six years.
This is the promise of AI. The incoming administration can build on recent successes and ensure that AI is used to the benefit of all Americans.
Dr. Alan R. Shark is executive director of CompTIAs Public Technology Institute and an associate professor at Schar School of Policy and Government at George Mason University. He is a Fellow of the National Academy of Public Administration, where he is Chair of the Standing Panel on Technology Leadership.
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Artificial Intelligence in Government and the Presidential Transition: Building on a Solid Foundation - Nextgov
Observability and Artificial Intelligence Have Become Essential to Managing Modern IT Environments – SPONSOR CONTENT FROM DYNATRACE – Harvard Business…
If you lead an IT, DevOps, or business operations team, youre probably working on a digital transformation and cloud migration strategy. Youre also likely doing it with scarce resources under the strain of shifting market needs and accelerated customer demands.
Your organizations success hinges on delivering differentiated, high-value digital experiences to customers and internal users. The applications and services that enable these experiences are built on multicloud environments that promise faster innovation and better business outcomes. But these dynamic environments also bring a scale, complexity, and frequency of change that have grown beyond humans capacity to manage.
The common approaches to monitoring these environments to build applications, optimize performance, and run operations are no longer effective. Just capturing data to display on a dashboard without providing automatic root-cause analysis or prioritizing discoveries by business impact just creates more noise than value.
Likewise, traditional tools and approaches are unable to automatically discover all services, processes, and interdependencies within a modern IT environment in real time, which results in blind spots. They also require manual configuration and instrumentation. Manual configurations may have worked in the era of on-premises data centers, but in the multicloud era, when applications and microservices come and go in seconds, manual efforts simply dont scale and instead steal time from innovation.
To manage these complex, cloud-native environments and to save time and resources for developing new innovations that deliver business impact, teams need solutions that rely on artificial intelligence (AI) and continuous automation to provide precise and intelligent answers.
A recent global survey of CIOs from large enterprises details why observability and AI for IT operations (AIOps) have become essential to managing modern IT environments:
According to the same survey, 70% of CIOs said their teams spend too much time doing manual tasks that could be automated, yet only 19% of all repeatable IT processes have been automated. CIOs view AI assistance as a solution93% said AI will be critical to their teams ability to cope with increasing workloads and deliver maximum value to the business.
To make the leap forward, companies are embracing AIOps. One example is ERT, a developer of the software and devices used by medical researchers in 75% of Food and Drug Administration-approved clinical trials in 2019. As the company adopted a cloud-native architecture running on Kubernetes, the IT team realized it needed to automate its software development processes.
ERTs teams now use one observability solution to monitor and automate DevOps processes and application delivery pipelines and to continuously watch for errors and degradation. Their AIOps solution automatically prioritizes any issues based on impact, saving developers time and ensuring they can find, understand, and resolve issues before they impact clinical trials. These processes have reduced from six to four weeks the time it takes ERT to deliver new applications, which means the company can help researchers get new, potentially lifesaving treatments out of the laboratory and into hospitals and pharmacies faster.
To understand the impact of IT on business outcomesincluding the significance of an outage, the value of a software update, or the level of customer engagement with a new feature or releaseIT, DevOps, and business operations teams need a single source of intelligence that provides precise answers prioritized by business impact and with root-cause determination.
As the pace of transformation accelerates, theres no time for silos, guessing, or finger-pointing, says Steve Tack, SVP product management at software intelligence company Dynatrace. Imagine having all teams in your organization on the same page all the time, with everyone using a common language, collaborating across teams, and speeding toward better business outcomes. This is possible with a platform that provides automatic and intelligent observability.
Tack pointed to footwear retailer Rack Room Shoes as one example of a company that transformed how its teams work by using a single source of software intelligence. As the company increased its investments in improving user experiences, its teams realized they needed to improve their understanding of how the performance of their new digital services impacted business key performance indicators, including e-commerce conversion rates and revenue. Their IT, developer, and business teams now rely on a single software intelligence platform to tie together data about their customers behavior with the applications they use and the cloud infrastructure on. As a result, the teams collaborate more effectively and optimize user experience more quickly, leaving 30% more time to focus on innovation, which has driven up their e-commerce conversions by 25%.
Regardless of your industry, success depends on accelerating digital transformation to drive new revenue streams, manage customer relationships, and keep employees productive. To achieve this, organizations are investing in multicloud platforms and cloud-native technologies. To maximize the benefits of these investments and to eliminate silos separating teams, organizations are increasingly looking to observability, automation, and AI-powered insights to automate IT operations so they can innovate faster and deliver better results.
Click here to learn how Dynatrace simplifies cloud complexity and accelerates digital transformation.
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Observability and Artificial Intelligence Have Become Essential to Managing Modern IT Environments - SPONSOR CONTENT FROM DYNATRACE - Harvard Business...
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring – Diagnostic Imaging
Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
Artificial intelligence has been a mainstay of the Radiological Society of North America (RSNA) annual meeting for nearly a decade. Each year brings new developments, reveals new capabilities and algorithms, and furthers the conversation about how these tools can help radiologists provide care.
This year, the conversation began to turn to what steps could come next for AI. Conversations about deployment, consolidation, and U.S. Food & Drug Administration clearance are now the norm as the technology becomes less of a novelty and more of a mainstay.
In this episode of The Reading Room, Diagnostic Imaging speaks with Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, about her reaction to what this years conference offered about these tools and what she sees on the horizon in 2021.
For additional RSNA coverage, click here.
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The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring - Diagnostic Imaging
Artificial intelligence in war: Human judgment as an organizational strength and a strategic liability – Brookings Institution
EXECUTIVE SUMMARY
Artificial intelligence has the potential to change the conduct of war. Recent excitement about AI is driven by advances in the ability to infer predictions from data. Yet this does not necessarily mean that machines can replace human decisionmakers. The effectiveness of AI depends not only on the sophistication of the technology but also on the ways in which organizations use it for particular tasks. In cases where decision problems are well-defined and plentiful relevant data is available, it may indeed be possible for machines to replace humans. In the military context, however, such situations are rare. Military problems tend to be more ambiguous while reliable data is sparse. Therefore, we expect AI to enhance the need for military personnel to determine which data to collect, which predictions to make, and which decisions to take.
The complementarity of machine prediction and human judgment has important implications for military organizations and strategy. If AI systems will depend heavily on human values and interpretations, then even junior personnel will need to be able to make sense of political considerations and the local context to guide AI in dynamic operational situations. Yet this in turn will generate incentives for adversaries to counter or undermine the human competencies that underwrite AI-enabled military advantages. If AI becomes good at predicting the solution to a given problem, for instance, a savvy adversary will attempt to change the problem. As such, AI-enabled conflicts have the potential to drag on with ambiguous results, embroiled in controversy and plagued by crises of legitimacy. For all of these reasons, we expect that greater reliance on AI for military power will make the human element in war even more important, not less.
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Artificial intelligence in war: Human judgment as an organizational strength and a strategic liability - Brookings Institution
Canada concludes inaugural plenary of the Global Partnership on Artificial Intelligence with international counterparts in Montral – Yahoo Finance
Canada concludes inaugural plenary of the Global Partnership on Artificial Intelligence with international counterparts in Montral
Canada NewsWire
OTTAWA, ON, Dec. 4, 2020
OTTAWA, ON, Dec. 4, 2020 /CNW/ - Artificial intelligence (AI) is fast becoming one of the most impactful technologies in the world today, changing the way people work, interact with each other and participate in the economy. Realizing the full potential of AI that benefits all citizens requires international collaboration and coordination.
On December 3 and 4, leading international AI experts from industry, civil society, academia and government, including 14 ministerial-level participants from nine countries, came together virtually for the first plenary of the Global Partnership on Artificial Intelligence (GPAI), during which they discussed how to guide their collective efforts to advance the responsible development and use of this technology.
The Honourable Navdeep Bains, Minister of Innovation, Science and Industry, spoke at the event's opening ceremonies and underscored how GPAI represents a model of partnership that will foster responsible AI innovation and economic growth, grounded in the shared values of human rights, inclusion and diversity.
He then joined Prime Minister Justin Trudeau and the President of France, Emmanuel Macron, who co-led the effort to establish GPAI through their 2018 and 2019 G7 presidencies. The leaders shared their vision for GPAI to guide the responsible development and use of AI globally.
GPAI is an initiative that leverages international collaboration on key research and applied projects focused on ensuring AI is human-centred by design and fostering public trust in its use. GPAI's founding members include Australia, Canada, France, Germany, India, Italy, Japan, Mexico, New Zealand, the Republic of Korea, Singapore, Slovenia, the United Kingdom, the United States of America and the European Union. On the margins of the plenary, the founding members welcomed Brazil, the Netherlands, Poland and Spain as new members of the Partnership.
Story continues
The plenary took place as part of the GPAI Montral Summit 2020, organized by the International Centre of Expertise in Montral for the Advancement of Artificial Intelligence. This is GPAI's first major event since its launch in June 2020, and it marks the beginning of Canada's role as the 20202021 Chair of the GPAI Council, which provides strategic direction to the Partnership.
Throughout the plenary, over 200 leading AI experts tackled core issues across several themes: the responsible adoption of AI, the use of AI in response to the COVID-19 pandemic, data governance, the future of work, and innovation and commercialization. The event gave the world's AI leaders a unique opportunity for exchange on the most promising ways to put AI into action and spark new projects on its impactful application.
As the event comes to a close, Canada looks forward to leading the Partnership in 20202021, during which it will leverage the principles of Canada's Digital Charter to promote a vision for a global AI ecosystem that enables responsible and trustworthy innovation, while fostering diversity and inclusion across the AI domain.
Quote
"AI is the single largest transformative technology in the world today. I'm proud to see that what began as a bilateral project between Canada and France is becoming a truly multilateral and multistakeholder initiative. Realizing the full potential of AI by creating benefits for all citizens requires international collaboration and coordination. GPAI will help shape a global AI ecosystem where innovation and growth are founded on trust and harnessed by our shared values of human rights, inclusion and diversity." The Honourable Navdeep Bains, Minister of Innovation, Science and Industry
Quick facts
Canada and France, along with international partners, have worked together since June 2018, including through Canada and France's G7 presidencies, to develop and launch GPAI.
GPAI will support the development and use of AI based on human rights, inclusion, diversity, innovation and economic growth, while seeking to address the United Nations Sustainable Development Goals.
Canada has a thriving AI ecosystem composed of more than 850 start-up companies, 20 public research labs, 75 incubators and accelerators, and 60 groups of investors from across the country, grouped in major hubs such as Montral, Toronto, Waterloo, Edmonton and Vancouver.
First announced in September 2019, the International Centre of Expertise in Montral for the Advancement of Artificial Intelligence (ICEMAI) is receiving up to $10 million over five years from the Government of Canada to support its and GPAI's activities. This is in addition to a $5-million grant previously announced by the Government of Quebec to create or attract an international AI organization.
ICEMAI also benefits from significant investments in AI in Canada and Quebec. In addition to receiving more than $900 million in foreign direct investment since 2017, the Montral AI ecosystem has benefited from nearly $1 billion in public funds, both from federal and Quebec initiatives. Of this funding, $40 million came from the Pan-Canadian Artificial Intelligence Strategy and $230 million came from the Innovation Superclusters Initiative that gave rise to the Montral Scale AI supercluster, which is focused on supply chains and SMEs. Over the next 10 years, these investments are expected to contribute $16.5 billion to Canada's GDP and help create over 16,000 jobs.
On June 15, 2020, Minister Bains and Quebec's Minister of International Relations and La Francophonie, Nadine Girault, made public a Canada-Quebec memorandum of understanding on GPAI that allows Quebec to participate in GPAI-related activities. The governments of Canada and Quebec will continue to involve and closely collaborate with other provincial and territorial governments to ensure Canada's work draws from the strong expertise in AI found from coast to coast.
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Canada concludes inaugural plenary of the Global Partnership on Artificial Intelligence with international counterparts in Montral - Yahoo Finance
Raytheon and C3.ai announce alliance on artificial intelligence solutions – C4ISRNet
WASHINGTON Raytheons intelligence and space business is partnering with C3.ai, a software company known for its predictive maintenance business with the U.S. Air Force, the companies announced Monday.
The alliance between C3.ai and Raytheon Intelligence and Space aims to speed up artificial intelligence adoption across the U.S. military. The partnership will pair Raytheons expertise in the defense and aerospace sector with C3.ais artificial intelligence development and applications.
The military and intelligence community have access to more data now than any time in history, but its more than theyre able to make quick use of, said David Appel, vice president of defense and civil solutions for space and C2 systems under Raytheon Intelligence and Space. Artificial intelligence can be used to help them make sense of that data, which will allow them to make smarter decisions faster on the battlefield. And thats just one of the benefits.
In recent years, C3.ai has positioned itself as a trusted partner of the Air Force, providing predictive maintenance capabilities for the services E-3, C-5 Galaxy, F-15, F-16, F-18 and F-35 aircraft. The Pentagons Silicon Valley arm that helped bridge C3.ai into the Pentagon, the Defense Innovation Unit, estimated that the program could save the service $15 billion annually in maintenance funds if it was scaled to the Defense Departments entire aircraft fleet.
In January, DIU awarded a five-year, $95 million contract to C3.ai for predictive maintenance. The alliance between the two companies will also focus on helping the intelligence community.
Raytheon and C3.ai are driven by similar purposes: Anticipating and solving our customers most difficult problems, said Thomas Siebel, CEO of C3.ai. Together, we offer an end-to-end enterprise AI platform and mission-tailored applications that will dramatically reduce cost and risk, accelerate adoption and deployment of AI solutions, and scale the impact of AI across any organization.
In September, the Air Forces rapid sustainment office selected C3.ais C3 AI Suite platform and C3 AI Readiness product to support predictive maintenance across the services enterprise.
Raytheon and C3.ai represent key partners for the U.S. Air Force, and specifically the Rapid Sustainment Office, in realizing the vision of harnessing AI to transform the military into a digital organization, said Nathan Parker, deputy program executive officer for the Air Force Rapid Sustainment Office. Fulfilling this vision of broad implementation requires identifying applicable use cases for AI across the Air Force, rapidly piloting solutions, and scaling successes across our enterprise to accelerate the transformation.
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Also on Monday, C3.ai announced that it will be launching an initial public offering. It expects shares to be valued between $31-$34.
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Raytheon and C3.ai announce alliance on artificial intelligence solutions - C4ISRNet
Translation Is Trickier For Business, And Artificial Intelligence Can Help – Forbes
Artificial Intelligence
Artificial intelligence (AI) for translation is something Google and other companies have provided for individuals. It can be accessed on your phone. However, translation is still a much larger and complex issue than many people realize. The business community has many complex and unique needs that add to the challenge of accurate and reliable translation, and AI is showing increasing capability.
One of the keys to business translation is the simple reality that each business sector has its own terms, phrases, and even idioms. A generic translation system in the cloud, trained widely by crowd sourcing or other public methods, wont have the accuracy required for business translation. In addition, the cloud itself is still a problem. Much of a businesses goals involve protecting intellectual property (IP). To do that, they want their information to stay on-premises, behind their firewalls.
Now throw in the complexity of privacy requirements such as the European Unions GDPR and Californias CCPA. Increasingly, governments are setting rules for where citizens data must be kept and what may be shared. The location and anonymization of information also adds to the challenge of a company understanding multilingual business.
Then theres collaboration. Just about everyone in business uses some electronic communications, whether it be emails and text messaging or more formal chat systems. Enhancing those applications with accurately, instant, translation can improve a global companys internal communications and drive success.
While ediscovery was an obvious entry point into business translation with AI, began JP Barazza, CIO, SYSTRAN. Imagine global development groups in such industries as high tech and biotech. They can become more efficient with the assistance of strong translation. Customer support is another horizontal where translation can be of great use.
As with cloud based models, the SYSTRAN system is using unsupervised learning. However, it uses a far more curated data set in order to train systems for each industry. The neural network is only a component of the logic of the system. Because of the specific terminology in many languages, procedural logic is used in pre- and post-processing around the network to help with the clear rules and terminology of business sectors. After all, theyre easier to manage for clearly defined linguistic convention while the neural network can handle the fluidity of the overall language.
One example of the need for rules is how names are used in American English and French French (yes, I had to do that). In the US, we regularly use a leaders name, such as President Biden. In France, news reports usually dont use names, but refer to titles, such as The President of the United States. Think about two-way translation. While it is simple to translate from English by dropping the name and expanding the title, said Jean Senellart, CEO. If we add a name when going from French into English, what happens when the president changes? The system would continue to add the previous presidents name until there was enough data to retrain the system. We made the decision to keep the French reference style when translating to English in order to remain accurate. The use of explicit rules is a clean way of addressing that issue.
That combination of neural network and procedural rules also provides flexibility to the company. A core system can be trained, with different plug-ins around it for different companies. That allows both a simpler development cycle and a cleaner way to provide updates. Specific corporate and industry rules can be added without having to retrain the deep learning system.
Increased accuracy is necessary for business. Consumers are willing to accept errors, as long as the general meaning is conveyed through translation, said Mr. Barazza. Business needs accuracy. Its not just for regulatory and contract compliance, a lack of accuracy can slow product development, lower safety, and create dissatisfied customers.
Because of that need for accuracy, and due to the state of the industry, theres another component to the solution. We are not yet at a point where the automated systems can be completely trusted. Humans must review the translations.
Within the system, at this point translation is complex and is focused on a small enough group of languages, so they are using pairwise engines. For example, one engine translates from English to French and the other translates from French to English. Training the systems uses a weird form of back propagation. In a single engine, back propagation means correcting results and feeding them back in as input. In translation, that means translating results back through the second engine, then correcting. Its more complex that that (at least for me), but I understand the basics of a very interesting loop where both engines help train each other.
That is the way translations are now done, but there is a change that will happen. That style means a lot of individual engines and the larger the number of languages, the increased permutations mean vast increase in the number of engines. One solution have been to use English as an intermediate language, translating everything through it to limit the different engines. That adds inefficiencies and inaccuracies. Facebook has recently announced a single model that can translate in all direction for multiple languages. While individuals are more comfortable with errors so thats a great place to test out such a model, eventually the technology will strengthen and corporate translation will benefit.
Business also drives non-AI design issue. SYSTRAN is not a pure cloud play. They must by hybrid, as on-premises computing is often required to meet privacy and other regulations.
Because of the state of the evolution of systems, including a lack of transparency in deep learning, no company is going to exclusively use AI driven translation for business and government. It will be used in respect to the 80/20 rule, where basic translation will save significant time and effort while humans will still be required to review and edit final versions of business and governmental translations.
Translation tools have made great advances in the last decade. Given the less rigid requirements in translation between individuals, it is no surprise that the initial focus has been on personal use. Technology has now advanced so that addressing the more formal requirements of business and governmental translations is now being addressed. Its early, but its looking good.
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Translation Is Trickier For Business, And Artificial Intelligence Can Help - Forbes