Page 3,747«..1020..3,7463,7473,7483,749..3,7603,770..»

The Tip-Off | Chess players proving they are and aren’t model citizens in a health crisis – SportsPro Media

Check, mates

Of the very, very few sporting events to take place anywhere in the last week, perhaps the most consequential is the ongoing FIDE Candidates Tournament. Eight chess grandmasters are facing off in Yekaterinburg, Russia for the right to play world champion Magnus Carlsen later this year.

FIDE and its promotional partner World Chess have been at pains to insist that it is doing all it can to ensure the safety of its players, with coronavirus tests, twice-daily health checks and abundant quantities of hand sanitiser. Curiously, in a sport that can so easily be played remotely, the competitors are sitting across the board inpersonbut some creative measures have been taken to encourage social distancing.

The most remarkable of these involved commissioning a collection of five-inch-tall Ken and Barbie-like models in each players image to be arranged for handshakes and other currently impossible protocols in official photographs. But as the Wall Street Journal has noted, there is one specific safety measure the proponents have not quite managed under intense concentration.

Of coursewe touch our faces, said world number sixMaximeVachier-Lagraveof France.That much is clear.

The sports industry is adapting to a period with pretty much no live sport. Staying a relevant, consistent, or even an uplifting part of peoples everyday lives through the social distancing and isolation needed to stave off the coronavirus pandemic is taking some creative thinking already.

For the most part, this has meant turning to two types of content: the archives, and shared digital events. Formula One andNascarhave launched enthusiastically into esports series involving current and former drivers as well as celebrities, with Formula Two driverGuanyuZhou winning the Bahrain Virtual Grand Prix on Sunday and theeNascariRacingPro Invitational Series has secured a linear TV deal with Fox Sports. Meanwhile organisations like world soccersFifahave opened up all manner of historic coverage for those who prefer their sport to be from the real world, albeit also from the past.

At this point, as can only be expected, these initiatives are fairly close to the minimum viable product phase and a high degree of improvisation. Still, there are a couple of questions that it will be worth asking as they continue.

First of all, who are they for and who will actually end up enjoying this content? Will esports output, for example, appeal to existing sports audiences hungry for something to follow, or existing esports communities hungry for something new? Do retro matches fill the gap for everyone, or just a specific niche? What can this tell us about the value of these types of offerings in the long term?

Secondly, what are rights holders aiming to accomplish? Retaining a measure of visibility and viability will be essential, to be sure, and there isa need to preserve a commercial link just as there is for thosepay-TVbroadcasters without live events to keepviewersonside withsubscription freezes. Yetasgovernment responses in country after country makeitlikelythat playing sport will take longer to return as a leisure activity than watching it on television, it will be just as important to thinkhard onhow toprotect some of those deeper connections, too.

Another event standing out across a barren sporting landscape is Major League Fishing in the US. 500,000 unique visitors watched a combined 19 million minutes of competition on the leagues in-house live stream,MLFNOW!,during the third round of the Bass Pro Tour in Lake Fork, Texasfrom 13thto 18thMarch, whenever that was.

Viewing numbers were up 20 per cent on events in February, which were themselves up 89 per cent on similar events year-on-year, while stage three also saw a 72 per cent rise insocial media impressions on the earlier contests.MLF says its sport is tailor-made for social distancing and took additional stepsto meet changing public health guidelines last week, including cancelling joint weigh-ins of each catch and altering accommodation arrangements. Round four of the Bass Pro Tour, ifyouneed things to put in your diary, runs from 3rdto 8thApril.

At a time when the current hiatus threatens financial insecurity for almost everyone, freelancers will be feeling that uncertainty more than most.

Credit is due, then, to the Professional Triathletes Organisation, which has instituted a series of radical measures to protect those men and women ranked 21 to 100 in its global rankings. Its Year-End Annual Bonus Programme will pay out immediately based on the current standings, rather than later in the season. The overall pot has grown from US$2 million to US$2.5 million, with the extra US$500,000 spread between those in those lower ranking brackets based on their position as of 1stJanuary or 15thMarch, whichever is higher.

Meanwhile, thetop ten triathletes in each list have committed to spending some of their downtime during the suspension of races making commercial appearances online to bring in additional funds for their fellow competitors.

On the subject of emergency pay responses, PGA Tour commissioner Jay Monahan is to forego his salary indefinitely in the wake of golfs flurry of cancellations and postponements, according to ESPN and Golfweek. Monahans gesturecomeswith a number of Tour executivestaking pay cuts of 25 per cent and all other staff facing salary freezes to 2019 levels.

Probably the closest any sports story has come to pulling attention away from any coronavirus-related updates was the news that Tom Bradys marathon, multiple Super Bowl-winning stint at the New England Patriots is at an end.

The Californian, who will be 43 by the time the new NFL season is scheduled to begin, has gone south to Florida and the Tampa Bay Buccaneers, and has made an immediate impact on the merchandising market. Fanatics has revealed that the top three bestselling jerseys across NFLShop.com and the Buccaneers online store have been mens and womens Tom Brady jerseys, with his day-over-day jersey sales rising 900 per cent.

The Bucs, unsurprisingly, are the strongest-performing team in the league in merchandise sales, selling twice as much inventory on Friday as the rest of the Fanatics network had in the previous two weeks. In Europe, ten times as many Brady jerseys were sold on Fridayas those for any other player.

View original post here:
The Tip-Off | Chess players proving they are and aren't model citizens in a health crisis - SportsPro Media

Read More..

Chess players violate the advice from the World health organization – The KXAN 36 News

the Whole atmosphere is hostile. All around us have masks, and there are security guards around us all the time. I will not be here, and I will not play.

It says Aleksandr Grisjtsjuk, one of the participants in the kandidatturneringen in chess.

It serves as a world cup qualifier, where the winner will face Magnus Carlsen for the world CHAMPIONSHIP match later in the year.

surgical masks: Some of the commentators use a face mask at work.

Photo: WORLD CHESS

the Organizer of the tournament has made several efforts to carry out the tournament, but the players are characterized by the situation.

After six rounds they have broken the advice from the World health organization (WHO) several times:

Several of the players takes care of the face (eyes, nose, and mouth). They sit closer than one metre from each other. Several of the players shake hands.

the HANDSHAKE: Anish Giri and Kirill Aleksejenko greet before the sixth round in kandidatturneringen.

Photo: WORLD CHESS dont Know if they will come home again

Players try to concentrate as normal. It involves taking in the face while they stare down at the chessboard and thinking on the next move.

For many of the players, this is their biggest tournament, and perhaps their only chance to qualify for a world CHAMPIONSHIP match. But after virusutbruddet that characterize the world view, also affects the worlds best players.

It is very difficult. It takes a lot of discipline not to follow what happens in the world, says Fabiano Caruana.

We have to follow what is happening. We can get information that we need to go somewhere, if not, were going to die. With so much at stake, it is important to keep up with the world than to play chess right now, says Dutch Anish Giri.

We are the only ones who hold on, everything else is stopped. It should have been deferred, says Grisjtsjuk.

MEASURES: Handshake before and after the parties are no longer mandatory, and the russians Jan Nepomnjasjtsjij and Aleksandr Grisjtsjuk greeted therefore.

Photo: Reuters

The sjakkekspert Atle Green is not concerned for the health of the eight players, but believes its about ethics.

It is an unfortunate symbolism. I think it is uncomfortable that would no longer be competitive is not in solidarity with the rest of the world, says Green.

All sport in Russia stopped

at the hotel in Yekaterinburg sits the eight sjakkspillerne in a folketomt local. The only thing that comes with is the security guards, and several of them have on a face mask, which creates a strange atmosphere.

It is absurd. But I can understand the organizer. All are concerned for the world, but I dont think we are worried about the eight. We are more concerned about those who are in real danger, says Green.

the Tournament is played at a luxury hotel in Yekaterinburg, east of the Urals, and at the entrance to Siberia. When the tournament started 17. march was all sports shut down in Russia.

In a press release before start-up, announced the organizer, the FIDE, that the tournament would go as planned. They pulled up that there were only eight players, and published at the same time that the public does not get access to spillelokalet. Officials and the press must follow strict rules.

The international sjakkforbundet have not responded to The request.

See original here:
Chess players violate the advice from the World health organization - The KXAN 36 News

Read More..

All India Chess Federation staff yet to receive salaries owing to issues between factions – Scroll.in

The All India Chess Federation staff are yet to receive their February salaries due to various issues including the internal squabble between the factions led by president PR Venketrama Raja and secretary Bharat Singh Chauhan.

While Chauhan said clearance of cheques to pay staff salaries and for other payments depended on its banker (Indian Bank), AICF Treasurer Kishor M Bandekar claimed the secretarys signature was mandatory and he was not signing citing lack of authority.

Chauhan said only the AICF general body had the authority to decide on the payments, adding unless it was held nothing could be done.

All the payments have to be approved by the general body. Unfortunately, we are not able to convene the general body, he said.

He also said the Indian bank officials had told him that they were seeking legal opinion on the clearance of AICF cheques.

Bandekar, on the other hand, said it was Chauhan who was not signing the cheques.

I have been requesting the secretary to sign but he has been refusing saying he does not have the authority and that the general body has to decide, he said.

Salaries of the staff for February are due. Also, March salaries will be due soon. There are other dues and the issue will be taken up with the bank.

Bandekar said the AICF Secretary and Treasurer are among the two authorised signatories for signing cheques on behalf of AICF and due to technical issues the payments are pending.

The factions led by Venketrama Raja and Chauhan have been at loggerheads over various issues. Their bickering saw the AICF elections being taken to court.

The Madras High Court had appointed retired Supreme Court Justice FM Ibrahim Kalifulla last month as Returning Officer to conduct the Federation elections.

He had declared Ajay H Patel elected as President, Chauhan as Secretary, Naresh Sharma as Treasurer, M Arun Singh as Joint Secretary, and Vipnesh Bharadwaj as Vice President.

After the new office-bearers assumed office, the High Court set aside their election and asked Kalifulla to convene a Special General Body Meeting to conduct fresh elections after an appeal by President Venketrama Raja.

Subsequently, the Supreme Court dismissed an appeal by Chauhan against the High Courts order.

Earlier this month, the court-appointed election returning officer had ordered All India Chess Federation to deposit Rs 58.50 lakh towards providing remuneration to himself and other charges to begin the election process according to the National Sports Development Code.

Meanwhile, the AICF has shut its office in Chennai and staff are working from home following the coronavirus pandemic, which has also resulted in cancellation/postponement of various events in India and across the world.

Link:
All India Chess Federation staff yet to receive salaries owing to issues between factions - Scroll.in

Read More..

The New ABC’s: Artificial Intelligence, Blockchain And How Each Complements The Other – Technology – United States – Mondaq News Alerts

To print this article, all you need is to be registered or login on Mondaq.com.

The terms "revolution" and "disruption" inthe context of technological innovation are probably bandied abouta bit more liberally than they should. Technological revolution anddisruption imply upheaval and systemic reevaluations of the waythat humans interact with industry and even each other. Actualtechnological advancement, however, moves at a much slower pace andtends to augment our current processes rather than to outrightdisplace them. Oftentimes, we fail to realize the ubiquity oflegacy systems in our everyday lives sometimes to our owndetriment.

Consider the keyboard. The QWERTY layout of keys is standard forEnglish keyboards across the world. Even though the layout remainsa mainstay of modern office setups, its origins trace back to themass popularization of a typewriter manufactured and sold by E.Remington & Sons in 1874.1 Urban legend has itthat the layout was designed to slow down typists from jammingtyping mechanisms, yet the reality reveals otherwise thelayout was actually designed to assist those transcribing messagesfrom Morse code.2 Once typists took to the format, thekeyboard, as we know it today, was embraced as a global standard even as the use of Morse code declined.3 LikeQWERTY, our familiarity and comfort with legacy systems hascontributed to their rise. These systems are varied in their scope,and they touch everything: healthcare, supply chains, our financialsystems and even the way we interact at a human level. However,their use and value may be tested sooner than we realize.

Artificial intelligence (AI) and blockchain technology(blockchain) are two novel innovations that offer the opportunityfor us to move beyond our legacy systems and streamline enterprisemanagement and compliance in ways previously unimaginable. However,their potential is often clouded by their "buzzword"status, with bad actors taking advantage of the hype. When one cutsthrough the haze, it becomes clear that these two technologies holdsignificant transformative potential. While these new innovationscan certainly function on their own, AI and blockchain alsocomplement one another in such ways that their combination offersbusiness solutions, not only the ability to build upon legacyenterprise systems but also the power to eventually upend them infavor of next level solutions. Getting to that point, however,takes time and is not without cost. While humans are generallyquick to embrace technological change, our regulatory frameworkstake longer to adapt. The need to address this constraint ispressing real market solutions for these technologies havestarted to come online, while regulatory opaqueness hurdles abound.As innovators seek to exploit the convergence of AI and blockchaininnovations, they must pay careful attention to overcome bothtechnical and regulatory hurdles that accompany them. Do sosuccessfully, and the rewards promise to be bountiful.

First, a bit of taxonomy is in order.

AI in a Nutshell:

Artificial Intelligence is "the capability of machine toimitate intelligent human behavior," such as learning,understanding language, solving problems, planning and identifyingobjects.4 More practically speaking, however,today's AI is actually mostly limited to if X, then Yvarieties of simple tasks. It is through supervised learning thatAI is "trained," and this process requires an enormousamount of data. For example, IBM's question-answeringsupercomputer Watson was able to beat Jeopardy! championsBrad Rutter and Ken Jennings in 2011, because Watson had been codedto understand simple questions by being fed countless iterationsand had access to vast knowledge in the form of digital dataLikewise, Google DeepMind's AlphaGo defeated the Go championLee Sedol in 2016, since AlphaGo had undergone countless instancesof Go scenarios and collected them as data. As such, mostimplementations of AI involve simple tasks, assuming that relevantinformation is readily accessible. In light of this, Andrew Ng, theStanford roboticist, noted that, "[i]f a typical person can doa mental task with less than one second of thought, we can probablyautomate it using AI either now or in the near future."5

Moreover, a significant portion of AI currently in use or beingdeveloped is based on "machine learning." Machinelearning is a method by which AI adapts its algorithms and modelsbased on exposure to new data thereby allowing AI to"learn" without being programmed to perform specifictasks. Developing high performance machine learning-based AI,therefore, requires substantial amounts of data. Data high in bothquality and quantity will lead to better AI, since an AI instancecan indiscriminately accept all data provided to it, and can refineand improve its algorithms to the extent of the provided data. Forexample, AI that visually distinguishes Labradors from other breedsof dogs will become better at its job the more it is exposed toclear and accurate pictures of Labradors.

It is in these data amalgamations that AI does its job best.Scanning and analyzing vast subsets of data is something that acomputer can do very rapidly as compared to a human. However, AI isnot perfect, and many of the pitfalls that AI is prone to are oftenthe result of the difficulty in conveying how humans processinformation in contrast to machines. One example of this phenomenonthat has dogged the technology has been AI's penchant for"hallucinations." An AI algorithm"hallucinates" when the input is interpreted by themachine into something that seems implausible to a human looking atthe same thing.6 Case in point, AI has interpreted animage of a turtle as that of a gun or a rifle as a helicopter.7 This occurs because machines arehypersensitive to, and interpret, the tiniest of pixel patternsthat we humans do not process. Because of the complexity of thisanalysis, developers are only now beginning to understand such AIphenomena.

When one moves beyond pictures of guns and turtles, however,AI's shortfalls can become much less innocuous. AI learning isbased on inputted data, yet much of this data reflects the inherentshortfalls and behaviors of everyday individuals. As such, withoutproper correction for bias and other human assumptions, AI can, forexample, perpetuate racial stereotypes and racial profiling.8 Therefore, proper care for what goesinto the system and who gets access to the outputs must be employedfor the ethical employment of AI, but therein lies an additionalproblem who has access to enough data to really take fulladvantage of and develop robust AI?

Not surprisingly, because large companies are better able tocollect and manage increasingly larger amounts of data thanindividuals or smaller entities, such companies have remainedbetter positioned in developing complex AI. In response to thistilted landscape, various private and public organizations,including the U.S. Department of Justice's Bureau of Justice,Google Scholar and the International Monetary Fund, have launchedopen source initiatives to make publicly available vast amounts ofdata that such organizations have collected over many years.

Blockchain in a Nutshell:

Blockchain technology as we know it today came onto the scene inlate 2009 with the rise of Bitcoin, perhaps the most famousapplication of the technology. Fundamentally, blockchain is a datastructure that makes it possible to create a tamper-proof,distributed, peer-to-peer system of ledgers containing immutable,time-stamped and cryptographically connected blocks of data. Inpractice, this means that data can be written only once onto aledger, which is then read-only for every user. However, many ofthe most utilized blockchain protocols, for example, the Bitcoin orEthereum networks, maintain and update their distributed ledgers ina decentralized manner, which stands in contrast to traditionalnetworks reliant on a trusted, centralized data repository.9 In structuring the network in thisway, these blockchain mechanisms function to remove the need for atrusted third party to handle and store transaction data. Instead,data are distributed so that every user has access to the sameinformation at the same time. In order to update a ledger'sdistributed information, the network employs pre-defined consensusmechanisms and military grade cryptography to prevent maliciousactors from going back and retroactively editing or tampering withpreviously recorded information. In most cases, networks are opensource, maintained by a dedicated community and made accessible toany connected device that can validate transactions on a ledger,which is referred to as a node.

Nevertheless, the decentralizing feature of blockchain comeswith significant resource and processing drawbacks. Manyblockchain-enabled platforms run very slowly and haveinteroperability and scalability problems. Moreover, these networksuse massive amounts of energy. For example, the Bitcoin networkrequires the expenditure of about 50 terawatt hours per year equivalent to the energy needs of the entire country ofSingapore.10 To ameliorate these problems,several market participants have developed enterprise blockchainswith permissioned networks. While many of them may be open source,the networks are led by known entities that determine who mayverify transactions on that blockchain, and, therefore, therequired consensus mechanisms are much more energy efficient.

Not unlike AI, a blockchain can also be coded with certainautomated processes to augment its recordkeeping abilities, and,arguably, it is these types of processes that contributed toblockchain's rise. That rise, some may say, began with theintroduction of the Ethereum network and its engineering around"smart contracts" a term used to describecomputer code that automatically executes all or part of anagreement and is stored on a blockchain-enabled platform. Smartcontracts are neither "contracts" in the sense of legallybinding agreements nor "smart" in employing applicationsof AI. Rather, they consist of coded automated parametersresponsive to what is recorded on a blockchain. For example, if theparties in a blockchain network have indicated, by initiating atransaction, that certain parameters have been met, the code willexecute the step or steps triggered by those coded parameters. Theinput parameters and the execution steps for smart contracts needto be specific the digital equivalent of if X, thenY statements. In other words, when required conditions havebeen met, a particular specified outcome occurs; in the same waythat a vending machine sells a can of soda once change has beendeposited, smart contracts allow title to digital assets to betransferred upon the occurrence of certain events. Nevertheless,the tasks that smart contracts are currently capable of performingare fairly rudimentary. As developers figure out how to expandtheir networks, integrate them with enterprise-level technologiesand develop more responsive smart contracts, there is every reasonto believe that smart contracts and their decentralizedapplications (d'Apps) will see increased adoption.

AI and blockchain technology may appear to be diametricopposites. AI is an active technologyitanalyzes what is around and formulates solutions based on thehistory of what it has been exposed to. By contrast, blockchain isdata agnostic with respect to what is written into it thetechnology bundle is largely passive. It is primarily inthat distinction that we find synergy, for each technology augmentsthe strengths and tempers the weaknesses of the other. For example,AI technology requires access to big data sets in order to learnand improve, yet many of the sources of these data sets are hiddenin proprietary silos. With blockchain, stakeholders are empoweredto contribute data to an openly available and distributed networkwith immutability of data as a core feature. With a potentiallylarger pool of data to work from, the machine learning mechanismsof a widely distributed, blockchain-enabled and AI-powered solutioncould improve far faster than that of a private data AIcounterpart. These technologies on their own are more limited.Blockchain technology, in and of itself, is not capable ofevaluating the accuracy of the data written into its immutablenetwork garbage in, garbage out. AI can, however, act as alearned gatekeeper for what information may come on and off thenetwork and from whom. Indeed, the interplay between these diversecapabilities will likely lead to improvements across a broad arrayof industries, each with unique challenges that the twotechnologies together may overcome.

Footnotes

1 See Rachel Metz, Why WeCan't Quit the QWERTY Keyboard, MIT Technology Review(Oct. 13, 2018), available at: https://www.technologyreview.com/s/611620/why-we-cant-quit-the-qwerty-keyboard/.

2 Alexis Madrigal, The Lies You'veBeen Told About the Origin of the QWERTY Keyboard, TheAtlantic (May 3, 2013), available at: https://www.theatlantic.com/technology/archive/2013/05/the-lies-youve-been-told-about-the-origin-of-the-qwerty-keyboard/275537/.

3 See Metz, supra note1.

4 See Artificial Intelligence,Merriam-Webster's Online Dictionary, Merriam-Webster (lastaccessed Mar. 27, 2019), available at: https://www.merriam-webster.com/dictionary/artificial%20intelligence.

5 See Andrew Ng, What ArtificialIntelligence Can and Can't Do Right Now, Harvard BusinessReview (Nov. 9, 2016), available at: https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now.

6 Louise Matsakis, Artificial IntelligenceMay Not Hallucinate After All, Wired (May 8, 2019),available at: https://www.wired.com/story/adversarial-examples-ai-may-not-hallucinate/.

7 Id.

8 Jerry Kaplan, Opinion: Why Your AI MightBe Racist, Washington Post (Dec. 17, 2018), availableat: https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/?noredirect=on&utm_term=.568983d5e3ec.

9 See Shanaan Cohsey, David A.Hoffman, Jeremy Sklaroff and David A. Wishnick, Coin-OperatedCapitalism, Penn. Inst. for L. & Econ. (No. 18-37) (Jul.17, 2018) at 12, available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3215345##.

10 See Bitcoin Energy ConsumptionIndex (last accessed May 13, 2019), available at: https://digiconomist.net/bitcoin-energy-consumption.

Keywords:Artificial Intelligence + Robotics,Blockchain, Fintech

Mofo Tech Blog - A blog dedicated to information,trend-spotting & analysis for science & tech-basedcompanies

Because of the generality of this update, the informationprovided herein may not be applicable in all situations and shouldnot be acted upon without specific legal advice based on particularsituations.

Morrison & Foerster LLP. All rights reserved

More:
The New ABC's: Artificial Intelligence, Blockchain And How Each Complements The Other - Technology - United States - Mondaq News Alerts

Read More..

Will COVID-19 Create a Big Moment for AI and Machine Learning? – Dice Insights

COVID-19 will change how the majority of us live and work, at least in the short term. Its also creating a challenge for tech companies such as Facebook, Twitter and Google that ordinarily rely on lots and lots of human labor to moderate content. Are A.I. and machine learning advanced enough to help these firms handle the disruption?

First, its worth noting that, although Facebook has instituted a sweeping work-from-home policy in order to protect its workers (along with Googleand a rising number of other firms), it initially required its contractors who moderate content to continue to come into the office. That situation only changed after protests,according toThe Intercept.

Now, Facebook is paying those contractors while they sit at home, since the nature of their work (scanning peoples posts for content that violates Facebooks terms of service) is extremely privacy-sensitive. Heres Facebooks statement:

For both our full-time employees and contract workforce there is some work that cannot be done from home due to safety, privacy and legal reasons. We have taken precautions to protect our workers by cutting down the number of people in any given office, implementing recommended work from home globally, physically spreading people out at any given office and doing additional cleaning. Given the rapidly evolving public health concerns, we are taking additional steps to protect our teams and will be working with our partners over the course of this week to send all contract workers who perform content review home, until further notice. Well ensure that all workers are paid during this time.

Facebook, Twitter, Reddit, and other companies are in the same proverbial boat: Theres an increasing need to police their respective platforms, if only to eliminate fake news about COVID-19, but the workers who handle such tasks cant necessarily do so from home, especially on their personal laptops. The potential solution? Artificial intelligence (A.I.) and machine-learning algorithms meant to scan questionable content and make a decision about whether to eliminate it.

HeresGoogles statement on the matter, via its YouTube Creator Blog.

Our Community Guidelines enforcement today is based on a combination of people and technology: Machine learning helps detect potentially harmful content and then sends it to human reviewers for assessment. As a result of the new measures were taking, we will temporarily start relying more on technology to help with some of the work normally done by reviewers. This means automated systems will start removing some content without human review, so we can continue to act quickly to remove violative content and protect our ecosystem, while we have workplace protections in place.

To be fair, the tech industry has been heading in this direction for some time. Relying on armies of human beings to read through every piece of content on the web is expensive, time-consuming, and prone to error. But A.I. and machine learning are still nascent, despite the hype. Google itself, in the aforementioned blog posting, pointed out how its automated systems may flag the wrong videos. Facebook is also receiving criticism that its automated anti-spam system is whacking the wrong posts, including those thatoffer vital information on the spread of COVID-19.

If the COVID-19 crisis drags on, though, more companies will no doubt turn to automation as a potential solution to disruptions in their workflow and other processes. That will force a steep learning curve; again and again, the rollout of A.I. platforms has demonstrated that, while the potential of the technology is there, implementation is often a rough and expensive processjust look at Google Duplex.

Membership has its benefits. Sign up for a free Dice profile, add your resume, discover great career insights and set your tech career in motion. Register now

Nonetheless, an aggressive embrace of A.I. will also create more opportunities for those technologists who have mastered A.I. and machine-learning skills of any sort; these folks may find themselves tasked with figuring out how to automate core processes in order to keep businesses running.

Before the virus emerged, BurningGlass (which analyzes millions of job postings from across the U.S.), estimated that jobs that involve A.I. would grow 40.1 percent over the next decade. That percentage could rise even higher if the crisis fundamentally alters how people across the world live and work. (The median salary for these positions is $105,007; for those with a PhD, it drifts up to $112,300.)

If youre trapped at home and have some time to learn a little bit more about A.I., it could be worth your time to explore online learning resources. For instance, theres aGooglecrash coursein machine learning. Hacker Noonalso offers an interesting breakdown ofmachine learningandartificial intelligence.Then theres Bloombergs Foundations of Machine Learning,a free online coursethat teaches advanced concepts such as optimization and kernel methods.

Continue reading here:
Will COVID-19 Create a Big Moment for AI and Machine Learning? - Dice Insights

Read More..

dotData Receives APN Machine Learning Competency Partner of the Year Award – WFMZ Allentown

SAN MATEO, Calif., March 25, 2020 /PRNewswire/ -- dotData, focused on delivering full-cycle data science automation and operationalization for the enterprise, today announced that Amazon Web Services (AWS) has awarded dotData with the APN Machine Learning (ML) Competency Partner of the Year Award for 2019.

The award recognizes dotData's rapid growth and success in the enterprise AI/ML market and its contribution to the AWS business in 2019. This award is a testament to dotData platform's ability to significantly accelerate and simplify development of new AI/ML use cases and deliver insights to enterprise customers. The award was announced today at the AWS Partner Summit Tokyo, currently taking place virtually from March 25 - April 10, 2020.

dotData announced in February 2020 that it had achieved AWS ML Competency status, only eight months after joining the AWS Partner Network (APN). The certification recognizes dotData as an APN Partner that accelerates the full-cycle ML and data science process and provides validation that dotData has deep expertise in artificial intelligence (AI) and ML on AWS and can deliver their organization's solutions seamlessly on AWS.

dotData provides solutions designed to improve the productivity of data science projects, which traditionally require extensive manual efforts from valuable and scarce enterprise resources. The platform automates the full life-cycle of the data science process, from business raw data through feature engineering to implementation of ML in production utilizing its proprietary AI technologies.

dotData's AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination, and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects.

"We are honored and proud to receive this award which recognizes our commitment to making AI and ML accessible to as many people in the enterprise as possible and our success in helping our enterprise customers meet their business goals," said Ryohei Fujimaki, founder and CEO of dotData. "As an APN ML Competency partner we have been able to deliver an outstanding product that dramatically accelerates the AI and ML initiatives of AWS users and maximizes their business impacts. We look forward to contributing to our customers' success bycollaborating with AWS."

AWS ML Competency Partners provide solutions that help organizations solve their data challenges and enable ML and data science workflows. The program is designed to highlight APN Partners who have demonstrated technical proficiency in specialized solution areas and helps customers find the most qualified organizations with deep expertise and proven customer success.

dotData democratizes data science by enabling existing resources to perform data science tasks, making enterprise data science scalable and sustainable. dotData automates up to 100 percent of the data science workflow, enabling users to connect directly to their enterprise data sources to discover and evaluate millions of features from complex table structures and huge data sets with minimal user input. dotData is also designed to operationalize data science by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflow. This can further automate the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, the data science task becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.

For more information or a demo of dotData's AI-powered full-cycle data science automation platform, please visit dotData.com.

About dotDatadotData is one of the first companies focused on full-cycle data science automation. Fortune 500 organizations around the world use dotData to accelerate their ML and AI projects and deliver higher business value. dotData's automated data science platform speeds time to value by accelerating, democratizing, augmenting and operationalizing the entire data science process, from raw business data through data and feature engineering to ML in production. With solutions designed to cater to the needs of both data scientists as well as citizen data scientists, dotData provides value across the entire organization.

dotData's unique AI-powered feature engineering delivers actionable business insights from relational, transactional, temporal, geo-locational, and text data. dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the "best machine learning platform" for 2019 by the AI breakthrough awards and was named an "emerging vendor to watch" by CRN in the big data space. For more information, visit http://www.dotdata.com, and join the conversation on Twitter and LinkedIn.

Read the rest here:
dotData Receives APN Machine Learning Competency Partner of the Year Award - WFMZ Allentown

Read More..

How our publisher harnessed machine learning to overhaul Techday websites – CFOtech New Zealand

Everyone is talking about Artificial intelligence (AI) and machine learning (ML) these days. Fitness devices are measuring our steps and analysing our daily health; map applications telling us the best way to get from A to B based on the trips of countless others before us; even the alarm app on our phone taking note of how long we sleep.

Here at Techday, we see examples every day of how AI and ML are shifting the landscape of modern technology into new and exciting territory. We have even seen the potential to harness itin our own operations.

This is the story of how our publishers passion for ML brought him to the CoderSchool in Ho Chi Minh City in Vietnam, and how we are incorporating AI and ML into Techday's business.

The publisher in question, Sean Mitchell, began his mission to bring cutting edge digital transformation technology to our business model two years ago.

Sean has always been passionate about systems and how automation is not just a nice to have but becoming a competitive must-have. So in 2018, he enrolled in a coding boot camp a full stack web development course.

He had never written code before this course, but by September 2018 he had finished and began overhauling Techday's websites and systems. We run 26 websites with complex systems for our editors, advertising and operations teams.

After redeveloping the look and feel of the Techday websites, he focused on our backend systems. This started pointing him in the direction of artificial intelligence and what a dramatic impact it can have on businesses.

"The goal was for our team to achieve more each day and be freed from the most mundane tasks. We knew we could achieve this with more automation and infusing machine learning into our systems," says Sean.

He couldnt find any suitable boot camps in New Zealand, and he didnt have time for a two-year computer science degree with little practical benefit.

Then he discovered a 12 week-long course on machine learning coding in Ho Chi Minh City taught byCoderSchoolwhich boasted the strong practical element he was looking for.

He signed up, flew over, settled in, and loved every gruelling hour.

Accommodation, food and transport were significantly cheaper than in New Zealand.

The whole course was taught in English, and Ho Chi Minh Citywas very friendly and welcoming to foreigners.

But the best part?

The course was 25% of the cost of a similar one in the US, says Sean.

Sean goes on to say the instructors were extremely helpful and had plenty of capacity to practice one-on-one tutoring, as there were four teachers and only 21 students in his course.

The course also included the basics of learning the python language for those who hadnt coded in it before, as well as a crash course in data analysis.

I can recommend the course to anyone who wants to practically implement AI and ML. This is a technical course with superb teachers and great course work, says Sean.

Sean is now back at Techday headquarters in New Zealand and has already put his studies into practical use.

Already, just a month after finishing the course, we are in thefinal steps of implementing machine learning into Techdays first AI workflow, says Sean.

Sean created a machine learning tool to read a draft of an editors story, and suggest keywords to use as tags for the story.

With over 6,000 stories written per annum this could add up to be a real human time saver.

Sean says the practical experience learnt atCoderSchool is proving more valuable every day.

If youre on the fence, then learning to code will change your life. It certainly did mine and our business will never be the same again.

If you want to learn more or apply to CoderSchool, visit their website.

UPDATE: Coderschool is continuing to teach temporarily in an online format during this time of crisis. More information is available on their website above.

More:
How our publisher harnessed machine learning to overhaul Techday websites - CFOtech New Zealand

Read More..

Machine Learning Engineer Interview Questions: What You Need to Know – Dice Insights

Along with artificial intelligence (A.I.), machine learning is regarded as one of the most in-demand areas for tech employment at the moment. Machine learning engineers develop algorithms and models that can adapt and learn from data. As a result, those who thrive in this discipline are generally skilled not only in computer science and programming, but also statistics, data science, deep learning, and problem solving.

According to Burning Glass, which collects and analyzes millions of job postings from across the country, the prospects for machine learning as an employer-desirable skill are quite good, with jobs projected to rise 36.5 percent over the next decade. Moreover, even those with relatively little machine-learning experience can pull down quite a solid median salary:

Dice Insights spoke with Oliver Sulley, director of Edge Tech Headhunters, to figure out how you should prepare, what youll be asked during an interviewand what you should say to grab the gig.

Youre going to be faced potentially by bosses who dont necessarily know what it is that youre doing, or dont understand ML and have just been [told] they need to get it in the business, Sulley said. Theyre being told by the transformation guys that they need to bring it on board.

As he explained, that means one of the key challenges facing machine learning engineers is determining what technology would be most beneficial to the employer, and being able to work as a cohesive team that may have been put together on very short notice.

What a lot of companies are looking to do is take data theyve collected and stored, and try and get them to build some sort of model that helps them predict what they can be doing in the future, Sulley said. For example, how to make their stock leaner, or predicting trends that could come up over they year that would change their need for services that they offer.

Sulley notes that machine learning engineers are in rarified air at themomentits a high-demand position, and lots of companies are eager to show theyve brought machine learning specialists onboard.

If theyre confident on their skills, then a lot of the time they have to make sure the role is right for them, Sulley said. Its more about the soft skills that are going to be important.

Many machine learning engineers are strong on the technical side, but they often have to interact with teams such as operations; as such, they need to be able to translate technical specifics into laymans terms and express how this data is going to benefit other areas of the company.

Building those soft skills, and making sure people understand how you will work in a team, is just as important at this moment in time, Sulley added.

There are quite a few different roles for machine learning engineers, and so its likely that all these questions could come upbut it will depend on the position. We find questions with more practical experience are more common, and therefore will ask questions related to past work and the individual contributions engineers have made, Sulley said.

For example:

Membership has its benefits. Sign up for a free Dice profile, add your resume, discover great career insights and set your tech career in motion. Register now

A lot of data engineering and machine learning roles involve working with different tech stacks, so its hard to nail down a hard and fast set of skills, as much depends on the company youre interviewing with.(If youre just starting out with machine learning, here are some resources that could prove useful.)

For example, if its a cloud based-role, a machine learning engineer is going to want to have experience with AWS and Azure; and for languages alone, Python and R are the most important, because thats what we see more and more in machine learning engineering, Sulley said. For deployment, Id say Docker, but it really depends on the persons background and what theyre looking to get into.

Sulley said ideal machine learning candidates posses a really analytical mind, as well as a passion for thinking about the world in terms of statistics.

Someone who can connect the dots and has a statistical mind, someone who has a head for numbers and who is interested in that outside of work, rather than someone who just considers it their job and what they do, he said.

As you can see from the following Burning Glass data, quite a few jobs now ask for machine-learning skills; if not essential, theyre often a nice to have for many employers that are thinking ahead.

Sulley suggests the questions you ask should be all about the technologyits about understanding what the companies are looking to build, what their vision is (and your potential contribution to it), and looking to see where your career will grow within that company.

You want to figure out whether youll have a clear progression forward, he said. From that, you will understand how much work theyre going to do with you. Find out what theyre really excited about, and that will help you figure out whether youll be a valued member of the team. Its a really exciting space, and they should be excited by the opportunities that come with bringing you onboard.

See original here:
Machine Learning Engineer Interview Questions: What You Need to Know - Dice Insights

Read More..

Put Your Money Where Your Strategy Is: Using Machine Learning to Analyze the Pentagon Budget – War on the Rocks

A masterpiece is how then-Deputy Defense Secretary Patrick Shanahan infamously described the Fiscal Year 2020 budget request. It would, he said, align defense spending with the U.S. National Defense Strategy both funding the future capabilities necessary to maintain an advantage over near-peer powers Russia and China, and maintaining readiness for ongoing counter-terror campaigns.

The result was underwhelming. While research and development funding increased in 2020, it did not represent the funding shift toward future capabilities that observers expected. Despite its massive size, the budget was insufficient to address the departments long-term challenges. Key emerging technologies identified by the department such as hypersonic weapons, artificial intelligence, quantum technologies, and directed-energy weapons still lacked a clear and sustained commitment to investment. It was clear that the Department of Defense did not make the difficult tradeoffs necessary to fund long-term modernization. The Congressional Budget Office further estimated that the cost of implementing the plans, which were in any case insufficient to meet the defense strategys requirements, would be about 2 percent higher than department estimates.

Has anything changed this year? The Department of Defense released its FY2021 budget request Feb. 10, outlining the departments spending priorities for the upcoming fiscal year. As is mentioned every year at its release, the proposed budget is an aspirational document the actual budget must be approved by Congress. Nevertheless, it is incredibly useful as a strategic document, in part because all programs are justified in descriptions of varying lengths in what are called budget justification books. After analyzing the 10,000-plus programs in the research, development, testing and evaluation budget justification books using a new machine learning model, it is clear that the newest budgets tepid funding for emerging defense technologies fails to shift the departments strategic direction toward long-range strategic competition with a peer or near-peer adversary.

Regardless of your beliefs about the optimal size of the defense budget or whether the 2018 National Defense Strategys focus on peer and near-peer conflict is justified, the Department of Defenses two most recent budget requests have been insufficient to implement the administrations stated modernization strategy fully.

To be clear, this is not a call to increase the Department of Defenses budget over its already-gargantuan $705.4 billion FY2021 request. Nor is this the only problem with the federal budget proposal, which included cuts to social safety net programs programs that are needed now more than ever to mitigate the effects from COVID-19. Instead, my goal is to demonstrate how the budget fails to fund its intended strategy despite its overall excess. Pentagon officials described the budget as funding an irreversible implementation of the National Defense Strategy, but that is only true in its funding for nuclear capabilities and, to some degree, for hypersonic weapons. Otherwise, it largely neglects emerging technologies.

A Budget for the Last War

The 2018 National Defense Strategy makes clear why emerging technologies are critical to the U.S. militarys long-term modernization and ability to compete with peer or near-peer adversaries. The document notes that advanced computing, big data analytics, artificial intelligence, autonomy, robotics, directed energy, hypersonics, and biotechnology are necessary to ensure we will be able to fight and win the wars of the future. The Government Accountability Office included similar technologies artificial intelligence, quantum information science, autonomous systems, hypersonic weapons, biotechnology, and more in a 2018 report on long-range emerging threats identified by federal agencies.

In the Department of Defenses budget press release, the department argued that despite overall flat funding levels, it made numerous hard choices to ensure that resources are directed toward the Departments highest priorities, particularly in technologies now termed advanced capabilities enablers. These technologies include hypersonic weapons, microelectronics/5G, autonomous systems, and artificial intelligence. Elaine McCusker, the acting undersecretary of defense (comptroller) and chief financial officer, argued, Any place where we have increases, so for hypersonics or AI for cyber, for nuclear, thats where the money went This budget is focused on the high-end fight. (McCuskers nomination for Department of Defense comptroller was withdrawn by the White House in early March because of her concerns over the 2019 suspension of defense funding for Ukraine.) Deputy Defense Secretary David L. Norquist noted that the budget request had the largest research and development request ever.

Despite this, the FY2021 budget is not a significant shift from the FY2020 budget in developing advanced capabilities for competition against a peer or near-peer. I analyzed data from the Army, Navy, Air Force, Missile Defense Agency, Office of the Secretary of Defense, and Defense Advanced Research Projects Agency budget justification books, and the department has still failed to realign its funding priorities toward the long-range emerging technologies that strategic documents suggest should be the highest priority. Aside from hypersonic weapons, which received already-expected funding request increases, most other types of emerging technologies remained mostly stagnant or actually declined from FY2020 request levels.

James Miller and Michael OHanlon argued in their analysis of the FY2020 budget, Desires for a larger force have been tacked onto more crucial matters of military innovation and that the department should instead prioritize quality over quantity. This criticism could be extended to the FY2021 budget, along with the indictment that military innovation itself wasnt fully prioritized either.

Breaking It Down

In this brief review, I attempt to outline funding changes for emerging technologies between the FY2020 and FY2021 budgets based on a machine learning text-classification model, while noting cornerstone programs in each category.

Lets start with the top-level numbers from the R1 document, which divides the budget into seven budget activities. Basic and applied defense research account for 2 percent and 5 percent of the overall FY2021 research and development budget, compared to 38 percent for operational systems development and 27 percent for advanced component development and prototypes. The latter two categories have grown from 2019, in both real terms and as a percentage of the budget, by 2 percent and 5 percent, respectively. These categories were both the largest overall budget activities and also received the largest percentage increases.

Federally funded basic research is critical because it helps develop the capacity for the next generation of applied research. Numerous studies have demonstrated the benefit of federally funded basic science research, with some estimates suggesting two-thirds of the technologies with the most far-reaching impact over the last 50 years [stemmed] from federally funded R&D at national laboratories and research universities. These technologies include the internet, robotics, and foundational subsystems for space-launch vehicles, among others. In fact, a 2019 study for the National Bureau of Economic Researchs working paper series found evidence that publicly funded investments in defense research had a crowding in effect, significantly increasing private-sector research and development from the recipient industry.

Concerns over the levels of basic research funding are not new. A 2015 report by the MIT Committee to Evaluate the Innovation Deficit argued that declining federal basic research could severely undermine long-term U.S. competitiveness, particularly for research areas that lack obvious real-world applications. This is particularly true given that the share of industry-funded basic research has collapsed, with the authors arguing that U.S. companies are left dependent on federally-funded, university-based basic research to fuel innovation. This shift means that federal support of basic research is even more tightly coupled to national economic competitiveness. A 2017 analysis of Americas artificial intelligence strategy recommended that the government [ensure] adequate funding for scientific research, averting the risks of an innovation deficit that could severely undermine long-term competitiveness. Data from the Organization for Economic Cooperation and Development shows that Chinese government research and development spending has already surpassed that of the United States, while Chinese business research and development expenditures are rapidly approaching U.S. levels.

While we may debate the precise levels of basic and applied research and development funding, there is little debate about its ability to produce spillover benefits for the rest of the economy and the public at large. In that sense, the slight declines in basic and applied research funding in both real terms and as a percentage of overall research and development funding hurt the United States in its long-term competition with other major powers.

Clean, Code, Classify

The Defense Departments budget justification books contain thousands of pages of descriptions spread across more than 20 separate PDFs. Each program description explains the progress made each year and justifies the funding request increase or decrease. There is a wealth of information about Department of Defense strategy in these documents, but it is difficult to assess departmental claims about funding for specific technologies or to analyze multiyear trends while the data is in PDF form.

To understand how funding changed for each type of emerging technology, I scraped and cleaned this information from the budget documents, then classified each research and development program into categories of emerging technologies (including artificial intelligence, biotechnologies, directed-energy weapons, hypersonic weapons and vehicles, quantum technologies, autonomous and swarming systems, microelectronics/5G, and non-emerging technology programs). I designed a random forest machine learning model to sort the remaining programs into these categories. This is an algorithm that uses hundreds of decision trees to identify which variables or words in a program description, in this case are most important for classifying data into groups.

There are many kinds of machine learning models that can be used to classify data. To choose one that would most effectively classify the program data, I started by hand-coding 1,200 programs to train three different kinds of models (random forest, k-nearest neighbors, and support vector machine), as well as for a model testing dataset. Each model would look at the term frequency-inverse document frequency (essentially, how often given words appear adjusted for how rarely they are used) of all the words in a programs description to decide how to classify each program. For example, for the Armys Long Range Hypersonic Weapon program, the model might have seen the words hypersonic, glide, and thermal in the description and guessed that it was most likely a hypersonic program. The random forest model slightly outperformed the support vector machine model and significantly outperformed the k-nearest neighbors model, as well as a simpler method that just looked for specific keywords in a program description.

Having chosen a machine-learning model to use, I set it to work classifying the remaining 10,000 programs. The final result is a large dataset of programs mentioned in the 2020 and 2021 research and development budgets, including their full descriptions, predicted category, and funding amount for the year of interest. This effort, however, should be viewed as only a rough estimate of how much money each emerging technology is getting. Even a fully hand-coded classification that didnt rely on a machine learning model would be challenged by sometimes-vague program descriptions and programs that fund multiple types of emerging technologies. For example, the Applied Research for the Advancement of S&T Priorities program funds projects across multiple categories, including electronic warfare, human systems, autonomy, and cyber advanced materials, biomedical, weapons, quantum, and command, control, communications, computers and intelligence. The model took a guess that the program was focused on quantum technologies, but that is clearly a difficult program to classify into a single category.

With the programs sorted and classified by the model, the variation in funding between types of emerging technologies became clear.

Hypersonic Boost-Glide Weapons Win Big

Both the official Department of Defense budget press release and the press briefing singled out hypersonic research and development investment. As one of the departments advanced capabilities enablers, hypersonic weapons, defenses, and related research received $3.2 billion in the FY2021 budget, which is nearly as much as the other three priorities mentioned in the press release combined (microelectronics/5G, autonomy, and artificial intelligence).

In the 2021 budget documents, there were 96 programs (compared with 60 in the 2020 budget) that the model classified as related to hypersonics based on their program descriptions, combining for $3.36 billion an increase from 2020s $2.72 billion. This increase was almost solely due to increases in three specific programs, and funding for air-breathing hypersonic weapons and combined-cycle engine developments was stagnant.

The three programs driving up the hypersonic budget are the Armys Long-Range Hypersonic Weapon, the Navys Conventional Prompt Strike, and the Air Forces Air-Launched Rapid Response Weapon program. The Long-Range Hypersonic Weapon received a $620.42 million funding increase to field an experimental prototype with residual combat capability. The Air-Launched Rapid Response Weapons $180.66 million increase was made possible by the removal of funding for the Air Forces Hypersonic Conventional Strike Weapon in FY2021 which saved $290 million compared with FY2020. This was an interesting decision worthy of further analysis, as the two competing programs seemed to differ in their ambition and technical risk; the Air-Launched Rapid Response Weapon program was designed for pushing the art-of-the-possible while the conventional strike weapon was focused on integrating already mature technologies. Conventional Prompt Strike received the largest 2021 funding request at $1 billion, an increase of $415.26 million over the 2020 request. Similar to the Army program, the Navys Conventional Prompt Strike increase was fueled by procurement of the Common Hypersonic Glide Body that the two programs share (along with a Navy-designed 34.5-inch booster), as well as testing and integration on guided missile submarines.

To be sure, the increase in hypersonic funding in the 2021 budget request is important for long-range modernization. However, some of the increases were already planned, and the current funding increase largely neglects air-breathing hypersonic weapons. For example, the Navys Conventional Prompt Strike 2021 budget request was just $20,000 more than anticipated in the 2020 budget. Programs that explicitly mention scramjet research declined from $156.2 million to $139.9 million.

In contrast to hypersonics, research and development funding for many other emerging technologies was stagnant or declined in the 2021 budget. Non-hypersonic emerging technologies increased from $7.89 billion in 2020 to only $7.97 billion in 2021, mostly due to increases in artificial intelligence-related programs.

Biotechnology, Quantum, Lasers Require Increased Funding

Source: Graphic by the author.

Directed-energy weapons funding fell slightly in the 2021 budget to $1.66 billion, from $1.74 billion in 2020. Notably, the Army is procuring three directed-energy prototypes to support the maneuver-short range air defense mission for $246 million. Several other programs are also noteworthy. The High Energy Power Scaling program ($105.41 million) will finalize designs and integrate systems into a prototype 300 kW-class high-energy laser, focusing on managing thermal blooming (a distortion caused by the laser heating the atmosphere through which it travels) for 300 and eventually 500 kW-class lasers. Second, the Air Forces Directed Energy/Electronic Combat program ($89.03 million) tests air-based directed-energy weapons for use in contested environments.

Quantum technologies funding increased by $109 million, to $367 million, in 2021. In general, quantum-related programs are more exploratory, focused on basic and applied research rather than fielding prototypes. They are also typically funded by the Office of the Secretary of Defense or the Defense Advanced Research Projects Agency rather than by the individual services, or they are bundled into larger programs that distribute funding to many emerging technologies. For example, several of the top 2021 programs that the model classified as quantum research and development based on their descriptions include the Office of the Secretary of Defenses Applied Research for the Advancement of S&T Priorities ($54.52 million), or the Defense Advanced Research Projects Agencys Functional Materials and Devices ($28.25 million). The increase in Department of Defense funding for quantum technologies is laudable, but given the potential disruptive ability of quantum technologies, the United States should further increase its federal funding for quantum research and development, guarantee stable long-term funding, and incentivize young researchers to enter the field. The FY2021 budgets funding increase is clearly a positive step, but quantum technologies revolutionary potential demands more funding than the category currently receives.

Biotechnologies increased from $969 million in 2020 to $1.05 billion in 2021 (my guess is that the model overestimated the funding for emerging biotech programs, by including research programs related to soldier health and medicine that involve established technologies). Analyses of defense biotechnology typically focus on the defense applications of human performance enhancement, synthetic biology, and gene-editing technology research. Previous analyses, including one from 2018 in War on the Rocks, have lamented the lack of a comprehensive strategy for biotechnology innovation, as well as funding uncertainties. The Center for Strategic and International Studies argued, Biotechnology remains an area of investment with respect to countering weapons of mass destruction but otherwise does not seem to be a significant priority in the defense budget. These concerns appear to have been well-founded. Funding has stagnated despite the enormous potential offered by biotechnologies like nanotubes, spider silk, engineered probiotics, and bio-based sensors, many of which could be critical enablers as components of other emerging technologies. For example, this estimate includes the interesting Persistent Aquatic Living Sensors program ($25.7 million) that attempts to use living organisms to detect submarines and unmanned underwater vehicles in littoral waters.

Programs classified as autonomous or swarming research and development declined from $3.5 billion to $2.8 billion in 2021. This includes the Army Robotic Combat Vehicle program (stagnant at $86.22 million from $89.18 million in 2020). The Skyborg autonomous attritable (a low-cost, unmanned system that doesnt have to be recovered after launch) drone program requested $40.9 million and also falls into the autonomy category, as do the Air Forces Golden Horde ($72.09 million), Office of the Secretary of Defenses manned-unmanned teaming Avatar program ($71.4 million), and the Navys Low-Cost UAV Swarming Technology (LOCUST) program ($34.79 million).

The programs sorted by the model into the artificial intelligence category increased from $1.36 billion to $1.98 billion in 2021. This increase is driven by an admirable proliferation of smaller programs 161 programs under $50 million, compared with 119 in 2020. However, as the Department of Defense reported that artificial intelligence research and development received only $841 million in the 2021 budget request, it is clear that the random forest model is picking up some false positives for artificial intelligence funding.

Some critics argue that federal funding risks duplicating artificial intelligence efforts in the commercial sector. There are several problems with this argument, however. A 2017 report on U.S. artificial intelligence strategy argued, There also tends to be shortfalls in the funding available to research and start-ups for which the potential for commercialization is limited or unlikely to be lucrative in the foreseeable future. Second, there are a number of technological, process, personnel, and cultural challenges in the transition of artificial intelligence technologies from commercial development to defense applications. Finally, the Trump administrations anti-immigration policies hamstring U.S. technological and industrial base development, particularly in artificial intelligence, as immigrants are responsible for one-quarter of startups in the United States.

The Neglected Long Term

While there are individual examples of important programs that advance the U.S. militarys long-term competitiveness, particularly for hypersonic weapons, the overall 2021 budget fails to shift its research and development funding toward emerging technologies and basic research.

While recognizing that the overall budget was essentially flat, it should not come as a surprise that research and development funding for emerging technologies was mostly flat as well. But the United States already spends far more on defense than any other country, and even with a flat budget, the allocation of funding for emerging technologies does not reflect an increased focus on long-term planning for high-end competition compared with the 2020 budget. Specifically, the United States should increase its funding for emerging technologies other than hypersonics directed energy, biotech, and quantum information sciences, as well as in basic scientific research even if it requires tradeoffs in other areas.

The problem isnt necessarily the year-to-year changes between the FY2020 and FY2021 budgets. Instead, the problem is that proposed FY2021 funding for emerging technologies continues the previous years underwhelming support for research and development relative to the Department of Defenses strategic goals. This is the critical point for my assessment of the budget: despite multiple opportunities to align funding with strategy, emerging technologies and basic research have not received the scale of investment that the National Defense Strategy argues they deserve.

Chad Peltier is a senior defense analyst at Janes, where he specializes in emerging defense technologies, Chinese military modernization, and data science. This article does not reflect the views of his employer.

Image: U.S. Army (Photo by Monica K. Guthrie)

Read the original:
Put Your Money Where Your Strategy Is: Using Machine Learning to Analyze the Pentagon Budget - War on the Rocks

Read More..

2020 Supply Chain Planning Value Matrix Underscores Benefits of Machine Learning and Customizable Integrations – Yahoo Finance

Nucleus Research identifies Blue Yonder, E2Open, Infor, Kinaxis, One Network and Vanguard as SCP Leaders

Nucleus Research today released the 2020 Supply Chain Planning (SCP) Technology Value Matrix, its assessment of the SCP market. For the report, Nucleus evaluated SCP vendors based on their products usability, functionality and overall value.

While other firms market reports position vendors based on analyst opinions, the Nucleus Value Matrix segments competitors based on usability, functionality and the value that customers realized from each products capabilities, measured with Nucleus rigorous ROI methodologies.

Nucleus named Blue Yonder, E2Open, Infor, Kinaxis, One Network and Vanguard as SCP leaders.

Supply chain planning has become critical for success as companies must maintain service levels in the face of resource constraints and external disturbances. Tight solution integrations and robust embedded analytics have become table stakes for supply chain planning systems, which can now differentiate based on go-to-market strategy and tactical focuses. Leading vendors have undertaken a "platform approach" to product delivery, providing solution flexibility that enables customers to drive long-term value by configuring deployments with their preferred blend of best practices and customizations.

"To support a broad range of planning capabilities, supply chain planning vendors must provide comprehensive product roadmaps," says Ian Campbell, CEO of Nucleus Research. "Now more than ever, customers demand the capability to prioritize tactical focuses and personalize SCP solutions with their own differentiators."

"In order to be resilient enough to handle external challenges, organizations must have robust plans in place for their supply chains," says Andrew MacMillen, analyst at Nucleus Research. "Proactive resource management has become essential for sustainable success and requires a greater level of collaboration across an organizations departments. Leading SCP solutions realize this, and can consolidate siloed data into a unified view to deliver value."

See the full report at: https://nucleusresearch.com/research/single/scp-technology-value-matrix-2020/

About Nucleus Research

Nucleus Research is a global provider of investigative, case-based technology research and advisory services. We deliver the numbers that drive business decisions. For more information, visit NucleusResearch.com or follow us on Twitter @NucleusResearch.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200324005437/en/

Contacts

Adam OuelletInkHousenucleus@inkhouse.com 978-413-4341

See the article here:
2020 Supply Chain Planning Value Matrix Underscores Benefits of Machine Learning and Customizable Integrations - Yahoo Finance

Read More..