CWI researchers, together with the department of radiation oncology of Amsterdam UMC, have developed software based on Artificial Intelligence (AI) that quickly proposes multiple radiation treatment plans for each patient. The software functions as a type of route planner for the doctor: it presents multiple plans based on the data of the patient that represent trade-offs between giving sufficient radiation dose to the tumor with as little damage possible to the surrounding organs. This not only helps the doctors to make plans faster, it will also improve plan quality. Amsterdam UMC has treated the first patient with a plan proposed by the new AI on March 17, 2020. The innovative technique will be used for the treatment of prostate cancer with internal radiation.

Radiation demands delicate maneuvering

Radiation is one of the most important treatments for cancer. In the case of prostate cancer, brachytherapy -a form of internal radiation with the use of catheters- is a very suitable treatment. Multiple catheters are inserted into a patient, through which a radio-active source is being led. After insertion of the catheters, a treatment plan is made by the doctors. In the case of brachytherapy this is being done while the patient is waiting with the catheters that have been inserted earlier. An uncomfortable situation, that preferably lasts as short as possible. Usually it takes the doctors quite some time to design a treatment plan for a specific patient. It takes the new software only a few minutes to come up with a whole range of treatment plans, however. Each plan states for how long the radioactive source should be focused on a specific area, in order to deliver a specific amount of radiation dose in that spot. The focus is on establishing a treatment that gives the desired amount of radiation dose to the tumor, while sparing as much of the surrounding healthy tissue as possible.

In the research and development stages, a blind test was performed with a team of radiation oncologists, showing both plans previously made and used in the clinic and new AI-made plans for the same patients. The radiation oncologists were very much convinced by the abilities of the new AI technique: they preferred an AI-based plan in 98% of the cases.

Peter Bosman, senior researcher at CWIs Life Sciences and Health group and project leader: Our form of AI delivers a spectrum of plans very fast, that represents the trade-offs between delivering sufficient radiation dose with as little damage to surrounding tissue as possible. This gives immediate insight into what is feasible for a specific patient. This relieves doctors from undertaking a complex approach to configuring a treatment plan using existing software that requires intensive human-computer interaction.

Amsterdam UMC has performed research in the area of computer-aided support for making radiation treatment plans for years already, says Arjen Bel, head of the clinical physics of the department of radiation oncology. The challenge is to make these plans quickly, as well as of high quality.

Bradley Pieters, radiation oncologist at Amsterdam UMC: The improved radiation treatment plans can lead to better results for patients with prostate cancer. The extra time allows us to deliver tailor-made plans for the patient. Besides that, we can now use our medical knowledge optimally, as well as extra knowledge about the patient that the computer does not have.

Unique collaboration

The development of the new software was made possible by a close research collaboration between CWIs Life Sciences and Health group, the department radiation oncology of Amsterdam UMC-location AMC, and Elekta, a company that delivers radiation equipment and software to hospitals. The team decided to develop software for this problem with a form of AI (evolutionary algorithms) at its core. These algorithms are very suitable to effectively and efficiently search for good solutions to complex problems, especially when there are multiple conflicting goals to be achieved.

The team especially focused on a form of evolutionary algorithms that arise from Bosmans long-running research line. These algorithms display intelligent search behavior. They have the ability to analyze a certain problem and subsequently teach themselves how they can come up with better solutions for that problem. The research team made special adjustments to search for configurations of treatment plans in the case of brachytherapy for prostate cancer as good as possible. They did that by letting the algorithm use knowledge about the build-up of radiation dose from the inserted catheters. Ultimately, much better results could be booked with this algorithm than with other algorithms.

Broadening the scope

One great advantage of the developed AI-software is that it can be expanded to other types of cancer relatively easily. Calculating a spectrum of possible treatment plans, considering the trade-off between a good chance of a successful treatment and possible side effects, is something that is needed in the making of radiation treatment plans for multiple forms of cancer. A follow-up project has already been planned. With funding of the Dutch Cancer Society (KWF Kankerbestrijding), CWI, Amsterdam UMC, and Elekta will expand this research to the area of internal radiation for cervical cancer, led by new project partner Leiden University Medical Center (LUMC).

Tanja Alderliesten, senior researcher at LUMC (previously Amsterdam UMC) and project leader: This time we will even execute a national validation study in order to create national impact. Business partner Elekta is working on the worldwide distribution of the software, to make sure the rest of the medical world can also profit from this innovation.

YouTube video about the project (in Dutch)

Interview with Peter Bosman in Bits&Chips (in Dutch)

Excerpt from:
Artificial Intelligence by CWI and Amsterdam UMC proposes the best radiation treatment plans in clinical practice for the first time - Centrum...

Updated: May 25, 2018:

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Compliance For A Digital World: BSA/AML The New ABC's: Artificial Intelligence, Blockchain And How Each Complements The Other - JD Supra

As many now know, last Thursday was an historic day in the stock market. On March 13, 2020, the S&P 500 plunged 9.5% in a single day, the worst daily drop since "Black Monday" in 1987. The plunge came the day after President Trump delivered an underwhelming speech that included a European travel ban. However, stocks rallied on Friday after news of more government stimulus, emergency measures to boost testing, and the purchasing of oil for the country's strategic reserve. Negotiations for a comprehensive support package for the economy are also ongoing.

However, one tech company was tuning out the noise. Semiconductor equipment maker Applied Materials (NASDAQ:AMAT) decided to announce an increase in its dividend on the exact same day the market went into freefall. Is that a sign of confidence, or foolishness?

Image source: Getty Images.

Applied Materials announced that it would raise its quarterly dividend by a penny, from $0.21 to $0.22, a 4.8% boost. Applied's dividend yield is now 1.86%, but that's with a very modest 27.5% payout ratio. The higher dividend will be paid out on June 11, to shareholders of record as of May 21. CEO Gary Dickerson said: "We are increasing the dividend based on our strong cash flow performance and ongoing commitment to return capital to shareholders. ... We believe the AI-Big Data era will create exciting long-term growth opportunities for Applied Materials."

Semiconductors and semiconductor equipment companies have historically been known to be cyclical parts of the tech industry. However, it appears Applied Materials believes the overarching trends for faster and smarter semiconductors should help the company power through a near-term economic disruption. As chip-makers make smaller and more advanced chips, Applied's machines are a necessary expenditure.

But can the long-term trends buffer the company in a times of a potential global recession?

It should be known that the semiconductor industry was already in a downturn last year in 2019, and was beginning to come out of it in early 2020. For Applied, last quarter's results exceeded the high end of its previous guidance, with revenue up 11% and earnings per share up 21%.On Feb. 12, management also guided for solid sequential growth in Q2 even while lowering its prior numbers by $300 million because of coronavirus as of that date.

On a Feb. 12 conference call with analysts, Dickerson reiterated that optimism:

We believe we can deliver strong double-digit growth in our semiconductor business this year as our unique solutions accelerate our customers' success in the AI-Big Data era... our current assessment is that the overall impact for fiscal 2020 will be minimal. However, with travel and logistics restrictions, we do expect changes in the timing of revenues during the year. We are actively managing the situation in collaboration with our customers and suppliers.

While many businesses across the world have seen severe interruptions, it's unclear if the chip industry will be affected as much as others, despite its reputation for cyclicality. While consumer-related electronics may take a temporary hit to demand, a more stay-at-home economy means the need for faster connections, which could actually increase demand for servers and base stations.

Memory chip research website DrameXchange released a report on March 13, outlining its current projections for the DRAM and NAND flash industries as of March 1, along with an updated "bear case" scenario should the coronavirus crisis escalate into a global recession, which was updated on March 12.

Category

Current 2020 Projections

Bear Case 2020 Projections

Notebook computer shipments

(2.6%)

(9%)

Server shipments

5.1%

3.1%

Smartphone shipments

(3.5%)

(7.5%)

DRAM price growth

30%

20%

NAND flash price growth

15%

(5%)

Data source: DrameXchange.

Notice that the enterprise-facing server industry looks poised to withstand a potential severe downturn much better than consumer-facing notebook or smartphone industry. In addition, DRAM prices are poised to increase in 2020 even in a recession, as prices had already crashed last year and the industry cut back on capacity. NAND flash had an earlier downturn than DRAM, and was already beginning to come out of it, so it has more potential with a decline in pricing.

In addition, the largest global foundry Taiwan Semiconductor (NYSE:TSM), just said on March 11 that its capacity for leading-edge 5nm chip production was already "fully booked," and that volume production would begin in April. That indicates continued strong demand for leading-edge logic chips.

So while there may be some more softness in certain parts of the chip industry, there are still relatively strong segments as well. Therefore, Applied may not face revenue declines in 2020, but rather a mere absence of previously forecast growth. Yet even if that happens, growth will likely be deferred to 2021, not totally lost, as eventually the demand for chips will increase.

After its decline, Applied Materials stock trades at just 17 times trailing earnings, and just 14.7 times projected 2020 earnings, though 2020 projections may come down. Still, that's a reasonable price to pay for Applied, especially in a zero-interest rate environment. The company has just as much cash as debt, and its recent dividend raise on the market's darkest day in recent history shows long-term confidence. Risk-tolerant investors with a long enough time horizon thus may want to give Applied -- and the entire chip sector -- a look after the dust settles.

Read the rest here:
This Artificial Intelligence Stock Raised Its Dividend on "Black Thursday" - Motley Fool

A new industrial revolution is taking place now and AI (AI) is transforming countries economically. The answer to the question of who is ahead and who is behind is determined by the new economic model based on this AI. Dozens of countries, from China to the U.S., from Finland to Kenya, are making significant investments in the area. It should be noted that by 2030, AI studies will generate a gross domestic product (GDP) greater than the current size of the Chinese economy ($15 trillion). From this new economy, China will generate nearly $7 trillion, the U.S. $3.7 trillion, Northern Europe $1.8 trillion, Africa-Oceania $1.2 trillion, the rest of Asia $0.9 trillion and Latin America $0.5 trillion. So, what will we do as a country? What kind of road map will we follow? How will we move forward in the digital economy revolution?

From driverless subways to flying taxis, from AI doctors to political consultants, from Smart TV announcers and robot muezzins to robot soldiers to autonomous attack planes, how ready are we for the new era?

Digital change and Turkey

AI creates change in society and adds new powers to people's power by enabling groundbreaking developments in areas such as healthcare, agriculture, education and transport. As AI technology continues to grow, we will work to ensure the ethical, pervasive and transparent dissemination of AI around the world, enabling everyone to take advantage of this technology, Microsoft President Brad Smith says.

Technological developments take hold of the entire world today, where digital transformation takes place fast. With new technologies; the processes of transformation and adaptation are taking place in economics, politics, healthcare and many other fields. In this context, studies on AI, 5G, Industry 4.0, big data and the "internet of things" (IoT) largely occupy the agenda. In particular, it is necessary to elaborate on AI studies here.

The studies of AI, which have undergone many ups and downs from the 1950s to the present, have entered a revolutionary process as of the 2010s with the use of machine learning and artificial neural networks.

Especially the fact that technologically and economically developed countries like the U.S., China and Germany have taken interest in AI studies both at the public and private levels and that they are competing with each other, has created a competitive environment across the globe.

The necessity of putting studies in a system within a specific plan has pushed countries to determine strategies and policies. The importance of the situation becomes evident considering that 35 countries have set a national AI strategy and international structures such as the U.N. and the EU joining the process as of January 2020.

Before going into practice in the context of Turkey and AI studies, the following should be noted: We are late in this race, but we can make up for it. Resources are limited, but progress can be made. Reasonable targets should be set. Target sectors should be determined.

The following suggestions should be noted in the area of practice. Research and development (R&D) funds should be created. Higher quality coding education should be offered in primary and secondary levels.

Besides, the field is not composed of engineering, so experts should be trained to interact around the world. Cooperation should be made with developed countries in this area.

It should be turned into a state policy. AI research centers should be established. Specialist import is required. The industrial incentive is required (on a sectoral basis).

Other critical suggestions

When the strategy documents released by other countries and the work they have performed are examined, we can list what needs to be done for Turkey as follows.

The impact of universities in the process should be boosted. AI workshops should be held urgently under the leadership of the academy. Encouraging the meetings to be held in the social sciences rather than in engineering is essential for ensuring that society can keep up with the age of AI and digital transformation. The results of the workshop should be presented to the Digital Transformation Office of the Presidency of the Republic of Turkey and should be taken as a basis in the strategy-building process.

A new academic title can be created to promote academic studies, boost international interest and ensure reverse brain drain. (E.g., the Alexander von Humboldt Professorship created by Germany in the context of AI strategy)

Science and social science departments based on AI-oriented studies should be established in universities and a skilled workforce should be trained in the fields of production, economics, management, law, philosophy and sociology.

The strategic plan should direct what kind of work will be done in what areas and clearly point out the opportunities. After studies are done in the determined fields, the sector can identify the advantageous positions internationally and carry out processes accordingly in different fields like military, healthcare, finance, education, environmental management, biotechnology, and industrial production, etc.

For society to adapt to the age of AI and digital transformation, an instructive website should be prepared and released to the public in visual and digital publications through public service ads.

Economically and technologically advanced countries such as the U.S., Germany, France and Canada attach importance to start-up companies in their strategies due to their advantageous positions. On the contrary, to use Turkeys economic resources in an effective, fast and solution-oriented way, instead of supporting start-up companies; companies that are already strong in the sector should be supported, employment incentives should be provided to ensure the employment of trained personnel in these companies.

To reduce the costs of start-up companies during the founding phase, cash incentives should be provided only for the supply of fixtures.

AI Made in Turkey trademark registration should be created. Manufactured products should be launched worldwide.

Workshops, meetings and consultations in public, private and academic fields should be increased and cooperation agreements should be made to ensure cooperation with leading countries in AI studies.

The use of AI-based programs in public institutions should be encouraged and necessary infrastructure transformation should be carried out.

All of the techno-cities owned by universities based in Istanbul should be collected in Istanbul Technopark. For a formation like Silicon Valley, a city other than Istanbul should be determined and the necessary material and financial infrastructure should be established.

To prevent the transfer of resources to inefficient work, the institutions and organizations that are provided with incentives should be supervised regularly.

Today, we are on the eve of a new era of geographical explorations; what we do will determine our future. If we believe, if we work hard, why not?

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Artificial intelligence: The new power dynamic of today - Daily Sabah

A cornerstone of the technology that is shifting the way we go about our lives is artificial intelligence (AI). Also known as machine intelligence or machine learning, AI is the development of computer-driven technology used to perform functions and tasks that previously required human intelligence. That said, AI ETFs are reaping the benefits.

Within the sprawling AI universe, there are four pillars: reactive machines, limited memory, theory of mind and self-awareness. An example of reactive machines would be the famous Deep Blue chess-playing supercomputer from IBM (NYSE:IBM) while autonomous and self-driving vehicles would be examples of technologies in the limited memory category.

Everyday applications of AI include Apples (NASDAQ:AAPL) SIRI, Alphabet (NASDAQ:GOOG, NASDAQ:GOOGL) search algorithim and Amazons (NASDAQ:AMZN) Alexa.

Those are basic forms of AI, but they serve as evidence of the markets growth and utility. Investors can harness those trends and more by taking advantage of the opportunities in AI ETFs.

That said, lets take a look at a few.

Source: Shutterstock

Expense Ratio: 0.75% per year, or $75 on a $10,000 investment

For investors looking for disruptive technology exposure, the actively managed ARK Innovation ETF (NYSEARCA:ARKK) fits the bill. The fund has a wide reach that encompasses not just pure AI, but industries using this next generation technology.

ARKK companies run the gamut of genomic firms, fintech providers, next generation internet (shared work and related infrastructure) and industrial innovation, among others. Like some other ARK funds, ARKK is know for its large weight to Tesla (NASDAQ:TSLA), which is more than 10%. However, it features plenty of other high fliers with dominant positioning in their respective markets, including Square (NYSE:SQ) and Illumina (NASDAQ:ILMN).

Moreover, some of ARKKs allure as an AI ETF is realized through its exposure to the deep-learning market a truly compelling long-term trend.

In fact, ARK believes that deep learning will be more impactful than the Internet:

The Internet created roughly $10 trillion in global equity market capitalization in 20 years. We believe that deep learning will have 3x that impact, adding $30 trillion to global equity markets over the next two decades.

Source: Shutterstock

Expense Ratio: 0.68% per year

The Global X Robotics & Artificial Intelligence ETF (NASDAQ:BOTZ) is an established giant in the world of AI ETFs with over $1 billion in assets under management and a track record spanning nearly four years.

The fund holds 38 stocks and its top holding is Nvidia (NASDAQ:NVDA), a name with deep AI credibility. That stock accounts for the bulk of the semiconductor exposure in BOTZ. Underscoring this funds diversity, BOTZ features allocations to 14 industry groups, including chip makers.

Importantly, BOTZ provides exposure to increasing efficiencies in the AI universe. In turn, these are widely viewed as a vital long-term driver of AI investment outcomes.

In the past, training robotics was laborious and required time, capital, and engineering expertise, but AI simulators are becoming increasingly accurate at transferring learning to real world applications, according to Global X research. These simulators can run thousands of iterative processes in seconds, creating vast amounts of training data.

Source: Shutterstock

Expense Ratio: 0.40%

The Defiance Quantum ETF (NYSEARCA:QTUM) is one of the premier AI ETFs when it comes to accessing the deep and machine learning themes. The funds underlying benchmark the BlueStar Quantum Computing and Machine Learning Index provides robust exposure to those markets.

Home to 60 stocks, QTUMs index gives the fund a deeper bench than many competing AI ETFs. QTUM itself has 84 holdings.

QTUM components are involved in quantum computing, which data indicates QTUMs exposure to this burgeoning theme could be a positive long-term driver.

The global commercial quantum computing market is expected to reach $1.3 billion by 2027 at a compound annual growth rate (CAGR) of 52.9% from 2022 to 2027 and $161 million by 2022 from $33.0 million in 2017 at a CAGR of 37.3% for the period 2017-2022, notes BCC Research.

Todd Shriber has been an InvestorPlace contributor since 2014. As of this writing, he did not hold a position in any of the aforementioned securities.

Read more:
3 AI ETFs Changing The World - Investorplace.com

Artificial intelligence is all the rage these days. In the popular media, regular cyber systems seem almost passe, as writers focus on AI and conjure up images of everything from real-life Terminator robots to more benign companions. In intelligence circles, Chinas uses of closed-circuit television, facial recognition technology, and other monitoring systems suggest the arrival of Big Brotherif not quite in 1984, then only about forty years later. At the Pentagon, legions of officers and analysts talk about the AI race with China, often with foreboding admonitions that the United States cannot afford to be second in class in this emerging realm of technology. In policy circles, people wonder about the ethics of AIsuch as whether we can really delegate to robots the ability to use lethal force against Americas enemies, however bad they may be. A new report by the Defense Innovation Board lays out broad principles for the future ethics of AI, but only in general terms that leave lots of further work to still be done.

What does it all really mean and is AI likely to be all its cracked up to be? We think the answer is complex and that a modest dose of cold water should be thrown on the subject. In fact, many of the AI systems being envisioned today will take decades to develop. Moreover, AI is often being confused with things it is not. Precision about the concept will be essential if we are to have intelligent discussions about how to research, develop, and regulate AI in the years ahead.

AI systems are basically computers that can learn how to do things through a process of trial and error with some mechanism for telling them when they are right and when they are wrongsuch as picking out missiles in photographs, or people in crowds, as with the Pentagon's "Project Maven"and then applying what they have learned to diagnose future data. In other words, with AI, the software is built by the machine itself, in effect. The broad computational approach for a given problem is determined in advance by real old-fashioned humans, but the actual algorithm is created through a process of trial and error by the computer as it ingests and processes huge amounts of data. The thought process of the machine is really not that sophisticated. It is developing artificial instincts more than intelligenceexamining huge amounts of raw data and figuring out how to recognize a cat in a photo or a missile launcher on a crowded highway rather than engaging in deep thought (at least for the foreseeable future).

This definition allows us quickly to identify some types of computer systems that are not, in fact, AI. They may be important, impressive, and crucial to the warfighter but they are not artificial intelligence because they do not create their own algorithms out of data and multiple iterations. There is no machine learning involved, to put it differently. As our colleague, Tom Stefanick, points out, there is a fundamental difference between advanced algorithms, which have been around for decades (though they are constantly improving, as computers get faster), and artificial intelligence. There is also a difference between an autonomous weapons system and AI-directed robotics.

For example, the computers that guide a cruise missile or a drone are not displaying AI. They follow an elaborate, but predetermined, script, using sensors to take in data and then putting it into computers, which then use software (developed by humans, in advance) to determine the right next move and the right place to detonate any weapons. This is autonomy. It is not AI.

Or, to use an example closer to home for most people, when your smartphone uses an app like Google Maps or Waze to recommend the fastest route between two points, this is not necessarily, AI either. There are only so many possible routes between two places. Yes, there may be dozens or hundredsbut the number is finite. As such, the computer in your phone can essentially look at each reasonable possibility separately, taking in data from the broader network that many other peoples phones contribute to factor traffic conditions into the computation. But the way the math is actually done is straightforward and predetermined.

Why is this important? For one thing, it should make us less breathless about AI, and see it as one element in a broader computer revolution that began in the second half of the twentieth century and picked up steam in this century. Also, it should help us see what may or may not be realistic and desirable to regulate in the realm of future warfare.

The former vice chairman of the joint chiefs of staff, Gen. Paul Selva, has recently argued that the United States could be about a decade away from having the capacity to build an autonomous robot that could decide when to shoot and whom to killthough he also asserted that the United States had no plans actually to build such a creature. But if you think about it differently, in some ways weve already had autonomous killing machines for a generation. That cruise missile we discussed above has been deployed since the 1970s. It has instructions to fly a given route and then detonate its warhead without any human in the loop. And by the 1990s, we knew how to build things like skeet submunitions that could loiter over a battlefield and look for warm objects like tanksusing software to decide when to then destroy them. So the killer machine was in effect already deciding for itself.

Even if General Selva's terminator is not built, robotics will in some cases likely be given greater decisionmaking authority to decide when to use force, since we have in effect already crossed over this threshold. This highly fraught subject requires careful ethical and legal oversight, to be sure, and the associated risks are serious. Yet the speed at which military operations must occur will create incentives not to have a person in the decisionmaking loop in many tactical settings. Whatever the United States may prefer, restrictions on automated uses of violent force would also appear relatively difficult to negotiate (even if desirable), given likely opposition from Russia and perhaps from other nations, as well as huge problems with verification.

For example, small robots that can operate as swarms on land, in the air or in the water may be given certain leeway to decide when to operate their lethal capabilities. By communicating with each other, and processing information about the enemy in real-time, they could concentrate attacks where defenses are weakest in a form of combat that John Allen and Amir Husain call hyperwar because of its speed and intensity. Other types of swarms could attack parked aircraft; even small explosives, precisely detonated, could disable wings or engines or produce secondary and much larger explosions. Many countries will have the capacity to do such things in the coming twenty years. Even if the United States tries to avoid using such swarms for lethal and offensive purposes, it may elect to employ them as defensive shields (perhaps against North Korean artillery attack against Seoul) or as jamming aids to accompany penetrating aircraft. With UAVs that can fly ten hours and one hundred kilometers now costing only in the hundreds of thousands of dollars, and quadcopters with ranges of a kilometer more or less costing in the hundreds of dollars, the trendlines are clearand the affordability of using many drones in an organized way is evident.

Where regulation may be possible, and ethically compelling, is limiting the geographic and temporal space where weapons driven by AI or other complex algorithms can use lethal force. For example, the swarms noted above might only be enabled near a ship, or in the skies near the DMZ in Korea, or within a small distance of a military airfield. It may also be smart to ban letting machines decide when to kill people. It might be tempting to use facial recognition technology on future robots to have them hunt the next bin Laden, Baghdadi, or Soleimani in a huge Mideastern city. But the potential for mistakes, for hacking, and for many other malfunctions may be too great to allow this kind of thing. It probably also makes sense to ban the use of AI to attack the nuclear command and control infrastructure of a major nuclear power. Such attempts could give rise to use them or lose them fears in a future crisis and thereby increase the risks of nuclear war.

We are in the early days of AI. We cant yet begin to foresee where its going and what it may make possible in ten or twenty or thirty years. But we can work harder to understand what it actually isand also think hard about how to put ethical boundaries on its future development and use. The future of warfare, for better or for worse, is literally at stake.

Retired Air Force Gen. Lori Robinson is a nonresident senior fellow on the Security and Strategy team in the Foreign Policy program at Brookings. She was commander of all air forces in the Pacific.

The rest is here:
The Evolution of Artificial Intelligence and Future of National Security - The National Interest

According to the renowned billionaire venture capitalist Tim Draper, decentralization is revolutionizing the currency systems around the world using the largest cryptocurrency by market capitalization, Bitcoin. Bitcoin, riding on the decentralization is converging with another technology that is also going to have a big impact which is artificial intelligence. Draper added:

Those technologies now have the ability to transform the biggest industries in the world. It is not just currency. It is banking and finance, insurance, real estate, healthcare, government. All those industries all in the trillions of dollars, they are hugely valuable, have the potential to be transformed by these new technologies.

Putting his comments into perspective in a new episode of 415 stories, Draper said that he can develop an insurance firm featuring an AI to detect fraud and utilizing smart contracts and Bitcoin, allow it to run on blockchain.

Follow this link:
Venture Capitalist Tim Draper: Bitcoin, decentralization and artificial intelligence could transform global industries - FXStreet

It was an AI that first saw it coming, or so the story goes. On December 30, an artificial-intelligence company called BlueDot, which usesmachine learningto monitor outbreaks of infectious diseases around the world, alerted clientsincluding various governments, hospitals, and businessesto an unusual bump in pneumonia cases in Wuhan, China. It would be another nine days before the World Health Organization officially flagged what weve all come to know asCovid-19.

That AI could spot an outbreak on the other side of the world is pretty amazing, and early warnings save lives. But how much has AI really helped in tackling the current outbreak?

The hype outstrips the reality. In fact, the narrative that has appeared in many news reports and breathless press releasesthat AI is a powerful new weapon against diseasesis only partly true and risks becoming counterproductive. For example, too much confidence in AIs capabilities could lead to ill-informed decisions that funnel public money to unproven AI companies at the expense of proven interventions.

So heres a reality check: AI will not save us from the coronaviruscertainly not this time. But theres every chance it will play a bigger role in future epidemicsif we make some big changes.

Read the original post

Read the original:
If we use it correctly, artificial intelligence could help us fight the next epidemic - Genetic Literacy Project

The Indian Institute of Technology-Madras is offering 150 hours of training to reskill women who have taken a break from their career.

The certification course includes artificial intelligence, machine learning, cyber security data science and big data.

The Career Back 2 Women is an initiative through the Institutes Digital Skills Academy. Candidates can choose the level of training.

The institute has tied up with the Forensic Intelligence Surveillance and Security Technologies to offer the programme.

IIT-M director Bhaskar Ramamurthi said, In the IT field, the technology changes are so rapid that they [women who take a break] are unable to get back to their careers as their skills are probably outdated. Despite this, their industry experience and knowledge about IT are immense and can be useful to many IT companies if they can fit into current requirements immediately. IIT-Madras is happy to pioneer this programme to help them get back to work and retrieve their careers.

Women who complete the advance module in select tracks would also receive assistance in job placement.

Digital Skills Academy, IIT-Madras, also plans to offer more courses at various levels for students and working professionals in association with NASSCOM and in partnership with training companies incubated at IIT Madras Research Park and industry partners.

K. Mangala Sunder, Head, Digital Skills Academy, said, IIT-M works with NASSCOM IT-ITeS Sector Skill Council to ensure that right industry partners are involved in training. Faculty from premier institutions provide fundamental knowledge to all learners.

According to C. Mohan Ram, Chief Mission Integrator and Innovator, FISST, all participants will take a 20-hour programme after which they can choose their area of specialisation. There are four tracks offered initially. Each track has basic and advanced modules.

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IIT-M to reskill women in artificial intelligence - The Hindu