Criterion 1

Recommendations

Info

ScientificThinking 4Jeff DeanHead of artificialintelligence atgoogle. He joinedgoogle in 1999,completed a ph.D.In computer scienceat the university ofwashington in 1996,and he has workedon a number ofprominent projectsin large-scale dataprocessing andmachine learning.The future of ArtificialIntelligenceIntorductionIn a technique called “Reinforcement Learning”, you have a set of actions you can take and try to predictwhat actions make the most sense. As you proceed, you begin to get a sense of whether the set of actionsyou took was a good idea or not.The rate of progress in the field of artificialintelligence is one of the most hotlycontested aspects of the ongoing boom inteaching computers and robots how to see theworld, make sense of it, and eventually performcomplex tasks both in the physical realm andthe virtual one. And just how fast the industry ismoving, and to what end, is typically measured notjust by actual product advancements and researchmilestones, but also by the prognostications andvoiced concerns of AI leaders, futurists, academics,economists, and policymakers. AI is going tochange the world — but how and when are stillopen questions.Here in this article, we can know more aboutsome of the major advances, and concerns, facingcurrent artificial intelligence research, and how itinterfaces with human society.The Field Of Machine Learning Has Made A LotOf Progress So, Where Do We See It Going Now?The fact that computer vision and these languagerelatedtasks are becoming more successful meansthat computers can now perceive the world aroundthem much better than they could before, and thathas implications beyond the boundaries of computerscience.We’ve seen significant developments in deep learning—essentially, a rebranding of artificial neural networks.These have been around for 30 or 40 years as a way ofdescribing abstract ways of learning from interestinginputs and outputs. But now, it turns out that deeplearning is useful for all kinds of problems in the fieldsof computer vision, speech recognition, languageunderstanding, and language translation.12 Issue 1 | March 2020
Now, all of a sudden, we can overcomemany of the grand challenges in thefields of design and engineering bymeans of machine learning. Questionslike “How can we make health care betterfor people?” or “How can we developor rework urban infrastructure?” areadjustable to machine learning. As anexample, self-driving vehicles are goingto be a big factor in rethinking how citiesshould be designed because they’regoing to be very different from cars thatwe drive.Machine Learning Was Built OnThe Model Of Showing ComputerLots Of Examples Until It FiguresOut The Connections. Are ThereOther Mechanisms Now?Most successful kinds of machinelearning are of that form: Collect a largedata set of inputs and outputs thatyou care about. It might be a bunch ofpictures, and each picture is labeled with“that’s a truck”, “that’s a pigeon”, “that’sa particular kind of monkey”. Throughexposure to many examples like that,the system can learn to generalize to acompletely new picture, now it can say:“Well, that picture is also a truck”. Thatprocess is called “supervised machinelearning”.But other kinds of machine learningare also making significant steps. Ina technique called “ReinforcementLearning”, you have a set of actions youcan take and try to predict what actionsmake the most sense. As you proceed,you begin to get a sense of whether theset of actions you took was a good ideaor not.How Can Data Sets AndAlgorithms Be Made Less ProneTo Unconscious Bias?Bias in machine learning algorithms is abig problem which we need to be awareof, and there’s an active area of researchabout how we can make these systemsin automated ways be less biased. Oneof the issues is that sometimes the datayou’re training on are biased; These aredata from the real world.Or you may have collected a data setthat doesn’t match the distribution ofdata that you trained the model on. Forexample, if you trained the model onphotos of North American weddings,and then started trying to recognizeIndian weddings, the distribution of thedata sets and the imagery you might seeare quite different. So, collecting datasets, that have the right distributionalproperties for the problem you careabout, is an important skill that youshould apply for machine learning.There are also techniques where youcan logarithmically adjust machinelearning models in such a way that allother things be equal, so you’ll have anequal chance of obtaining a certain kindof outcome.What Is Your Take On Privacy InThe Age Of Machine Learning?As people take advantage of many newonline services out there, they createdata about how those services are beingused. And often companies use thosedata to improve their services, such asunderstanding that when people watchthis kind of videos, they may also beinterested in that kind. Or when theymistype this word and correct it later,maybe we can learn a spelling correctionsystem that helps all users based onthe behavior of how people do spellingcorrections.I believe that people should have controlover whether those data are collected,and if they are, people should have theability to delete them to take effect in atimely manner as people update theirmachine learning systems.Are There Ways MachineLearning Can Help People BreakOut Of Silos?Many products have more of a focuson recommendations than on breadthof sampling. It’s very easy for machinelearning to operate so that if you likethis sort of thing, they just recommendmore of that thing. I think there isan opportunity in the algorithms toencourage diversity of the things thatpeople are exposed to. And it’s definitelysomething that we think about at theproduct level. We want to expose peopleto the ideas they might find thoughtprovoking,as opposed to things thatcompletely agree with their currentthinking.Even Though Machines Are NowTraining Themselves, MachineLearning Programs Are StillCreated By Human Beings.Do We Place An Emphasis OnHaving A Diversity Of PeopleFrom Various Backgrounds AndCultures On Our Team To EnsureThat We Produce More PowerfulResearch?Yes, absolutely. Computer scienceand machine learning are creatingreally interesting new products andapplications in the world which areaffecting billions of people. So youwant those services to be created bythe breadth of people that use them.It’s important to find talented people allover the world with all kinds of differentbackgrounds to help create new kindsof machine learning algorithms, newkinds of approaches, and new kinds ofproducts.As an example of that, on June 2018,we announced our first research lab inAfrica, in Accra, Ghana. I’m really excitedto have that. The field of machinelearning in the African continent hasbeen growing very quickly, as there’s alot of interest there, and a great manyresearchers, young students, andyoung practitioners. I went to CapeTown, South Africa, to participate inDeep Learning Indaba 2018 with about500 people from 40 Spanish countriesaround Africa all coming together tohear a series of lectures and that was afantastic experience.Issue 1 | March 202013
Page 3 and 4: Issue 1 | March 20201
Page 5 and 6: ForewordTrust Your Abilities“Dict
Page 7 and 8: InterviewI started my career path i
Page 9 and 10: “FailureDoesn’t MeanYou Are AFa
Page 11 and 12: you find less interesting,you’ll
Page 13: MatchingYou try to make a SWOT anal
Page 19 and 20: Wi-Fi, these transformative technol
Page 21 and 22: Like all Schema, the FAQ content mu
Page 25 and 26: 2- When I Learn A New VocabularyWor
Page 27 and 28: 3. Recognize The Power OfInfluence
Page 29 and 30: Don’t Be A RoombaBelieve or not,
Page 31 and 32: Sponsor(Platinum)Issue 1 | March 20

Criterion 1

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?