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Explore why AI matters in 10 clicks -
1   2  34 5 6 7 89  


    10 

at the same time this article helps understand ways in which engineers link far more than AI if they have unique human purpose for being Masayoshi Son, sir fazle abed;  jack ma ; jingfang hao;  fei-fei li ; melinda gatesjin keyu;   priscilla chan ; Sheikha Moza ; BezosLeana WenOla Brown;  Audrey ChengEva Vertes ; Eleni Gabre-Madhin;  Monica YunusTian WeiYao Zhang ; Ying Lowrey ; Cairui Fu ; Gloria Ai; Kai-Fu Leeandtrew ng ; Intel delaney axios  leonsis bedy yang ckgsb  tsnnghua  tsinghua-mit jerry yang  nilekani  berners lee 

2019 CES: includes women in 2019's keynotes?- IBM Chairman, President and CEO, Ginni Rometty,as will AMD President and CEO Dr. Lisa Su.

www.worldclassnations.com what i loved about americas 1960s moon race-demonstration " network 5000 brains round one goal- no corporate , government or academic silos - new tech can make almost any goal-directed mission humanly possible- today hundreds of DC ( www.digitalcooperation.org ) moon races started with thousand times more tech than 1960s- all benefit sustainability on earth blending robots or www.AIdemocracy.com with humanI Americans not in one most exciting race because their media and leaders dont believe in cooperation, and they wont free 5000 young brains to network around a goal that might take 7 years not 90 days or one trading day to speculate round- when i say americans - we the ordinary people/families/communities want to free our kids, we are being totally mis-served by big vested interest that turned DC into an abortion of every youthful american dream. I am very happy to be wrong- show me the cooperation bookmark but meanwhile here are examples of some of china's greatest moon races down on earth - they come in 5 robotbeltroads only one of which china can do without its neighbors youth linking in -after all china needs its fifth of the world population to HumanI, it wants India's fifth to join in and next..

Robots AI with HumanI - China has at least 5 AIBeltRoads only one of which is focused mainly on its fifth of the world's population- go south west to invite India's fifth of world to join in DigitalCooperation especially with the greatest dayaset compiled India's billion person digital id thanks to tech wizards linkedin by Nilekani https://www.linkedin.com/groups/3274087/; North to understand polar and massive landlocked BeltRoads cyber etc needs to make sure AI is safe; go east and see how china connects with the great humanoid AI and the worlds greatest youth expos- how can Ma's sponsorship of Tokyo Olympics help AI to celebrate every youth-in-community -more on Ma's first 100 AI and DCooperation here labs ...there are many ways to AI- for example you can ask a robot to learn for you - it now costs less than 7000 dollars (source joi ito MIT media lab at NY Japan Society) for a child to send a small smart experiment into space; or we can ask a Hugo award-winning female chinese science fiction writer to write a loveq short story and we can give it to one of UN or WEF friends of jack ma's universities www.alibabauni.com  or intercity hubs or www.maOlympics.com expos of youth  to tell us whats not possible- this month china started AI at primary level- its important that teacher training robots learn with young girls first - this will help china collaborate with any nation that wants to go beyond

hashtag#metoo hashtag#theeconomist @obamauni to women hold up half the sky -

Download AI report 2018for data on uses of AI =lead authors include Yoav Shoham (Chair) Stanford University Raymond Perrault SRI International Erik Brynjolfsson MIT Jack Clark OpenAI James Manyika McKinsey Global Institute Juan Carlos Niebles Stanford University Terah Lyons Partnership On AI John Etchemendy Stanford University Barbara Grosz Harvard University

singularity - in 2018 it was possible to produce one silicon chip aith 30 billion binary synapses -the same capacity as a human brain - in 30 years the capacity of a chip will be one billion bigger than a human brain -founder softbank 

can women AI the  world- jan news -via axios Liz Fong-Jones,  leaving the tech giant to work at a startup.

Why it matters: Fong-Jones was early to challenge her employer on a range of issues from sexual harassment to its work on controversial projects.

What's next: Fong-Jones tells Axios that she is headed to be the first developer advocate at Honeycomb.io, a startup that aims to make distributed systems understandable by engineers.

  • She says what draws her to the company, in addition to the match with her expertise, is the leadership roles held by women and its commitment to diversity and corporate ethics.


— Liz Fong-Jones
  • She said that activism can be hard to sustain and she didn't see a way to remain at Google without burning out.
 aidefine.JPG






many people to recommend you meet in beijing if you are passing through (beijing launched the world first AI curriculum for primary this week)

we absolutely need to linking all tech people and all people representing those with one or more of 5 senses not standard - do this in 2019 and all or artificial intelligence can go well -otherwise we'll never know what we are doing with more robots than humans once G5 is scaling

best chris  AI round the world 




for those wanting high school curricula i would suggest building from here unless someone wants to be our guide

Learning Artificial Intelligence


Home About

Machine Learning and AI Curriculum

Mar 26, 2017

I am nearly finished with my coursework in machine learning and artificial intelligence. As I wrap up, I thought I would recommend a curriculum for others that are just beginning a similar journey - incorporating the benefit of hindsight. My guiding philosophy is to build strong fundamental understanding. This leads to intuition, and ultimately the ability to creatively solve new problems in multiple areas.

I completed about 25 MOOCs. The courses listed below are the best of the best. I left off survey courses that that didn’t promote deep understanding. Also, there are a lot of duplicate courses out there because this is a hot area right now. While it can be useful seeing the same material presented from different perspectives, I chose the best presentation below.

I’d recommend taking the courses in the order I’ve listed them. In some cases, the order is to avoid struggles due to missing prerequisites. In other cases, the ordering will give you a better perspective on what is to come. I also avoided front-loading all the math courses - it is important to have variety.

Important Note: It takes a high level of motivation and discipline to learn this material. I worked many extra problems with pencil and paper for a fuller understanding. So, dig in when you don’t understand something. Don’t guess and check the multiple choice problems and move on. Your goal is not to earn a bunch of MOOC certificates, your goal is to learn these subjects.

Machine Learning (Stanford / Coursera)

This is the famous Andrew Ng course. In fact, if you’re reading this, it is quite likely you’ve already completed this one. This is an excellent introduction to Machine Learning, but it is not a university level course like Stanford’s on-campus CS229. Nevertheless, I still think this course is a great place to start.

The lectures are fantastic with plenty of practical advice and a bit of theory. The programming assignment all use MATLAB or Octave. MATLAB tutorials are provided at the start, with no prior experience required.

In the assignments, you build the classic machine learning algorithms almost from scratch using basic matrix operations. The course covers all the greatest hits: linear regression, logistic regression, gradient decent, regularization, neural networks, support vector machines, bias vs. variance and so on.

This course has no serious math or programming prerequisites. You will leave with incredible practical advice on applying machine learning. And you will gain skills that you can apply right away in your current job or studies. This course lays a great foundation, but you will need to take a more intense machine learning course later in the curriculum.

Computer Science and Programming Using Python (MIT / EdX)

Python is the language for AI and Machine Learning. This isn’t a Python course, but you will learn Python well enough to succeed in the upcoming courses. The course develops a basic foundation in algorithmic thinking. You will finish with a solid understanding of object-oriented programming.

This is a high-quality MOOC. It uses the EdX platform beautifully. The lectures are excellent, and the programming assignments drive understanding. After taking this course you will be ahead of many of your classmates in the MOOCs to come.

Calculus 1A: Differentiation, and 1B: Integration (MIT / EdX)

If you are just a couple years out of college, you can probably skip these two courses. If not, and your calculus is rusty, you will need to take these. Otherwise, you will struggle with parts of probability, and other courses that follow. Several courses take for granted a level of mathematical maturity. So, getting comfortable manipulating equations again will pay off. These two courses will sharpen the skills you will need.

Like most of the MIT EdX courses, these two are excellent. Each course is 1/3rd of MIT’s first-semester calculus. These are original MIT lectures that have been sliced into the EdX format. Professor David Jerison is an engaging lecturer. I enjoy learning from someone so at ease with the material, who is teaching only with their voice and a piece of chalk.

Introduction to Probability (MIT / EdX)

Until the 1980s, Artificial Intelligence was hampered by its reliance on boolean rule-based systems. There have been many breakthroughs in AI, but the introduction of probabilistic reasoning may be one of the most important. This article on Professor Judea Pearl’s work and his ACM Turing Award does a nice job explaining the importance of introducing uncertainty to AI.

You are going to see a lot of probability in the coming courses. It takes time, but you will need to become very comfortable with random variables, variance, expectations, Bayesian inference, Markov chains and so on. This one is not easy, but it will pay big dividends. John Tsitsiklis’ lectures are incredible and you should own a copy of the text book.

Artificial Intelligence (UC Berkeley CS188x)

Not long after completing this course, I decided to change careers and focus on machine learning and AI. Suffice to say, I thought it was incredible. I have linked to the version that was taught by Professor Dan Klein and Professor Pieter Abbeel. Although this course was once offered on the EdX platform, it is now only available as self-study. While the deadlines and structure of a MOOC are nice, if you made it this far, you have the discipline required to complete this course.

I remember watching in awe the result of wrapping a reinforcement learning engine around a simulated one-armed robot. This robot flopped its arm around and, every once in a while, it got lucky and dragged itself forward. The robot earns a reward when it stumbled forward. It was like watching a child learn how to crawl. Eventually, this block robot, with a single hinged arm, was dragging itself almost gracefully across my computer screen.

The true highlight of the course are the Pacman-themed programming assignments. As the course goes on you endow your Pacman agent with increasing levels of intelligence. These labs are tough but rewarding. I usually lost track of time while completing them, often staying up late into the night.

Linear Algebra (MIT OPENCOURSEWARE)

Linear algebra is an essential tool for machine learning. Again, this is not a MOOC with deadlines, discussion forums and quizzes every 10 minutes. You need to watch the lectures, do the same problem sets as the MIT undergraduates, and take the exams. But the resources are available to make this very doable. Professor Strang’s lectures are great - I enjoyed them more and more as the course progressed. And Strang’s text bookis excellent and fully worked solutions are available for all the problems.

No longer will you think of matrix multiplication as a series of repeated dot-products. You will start to think in the row- and column-space. You will understand the beauty and importance of eigenvalues and eigenvectors. Projections and their relationship to linear regression will make perfect sense. The course closes with a beautiful treatment of singular value decomposition (SVD).

Algorithms (Stanford / Coursera)

To bring your ideas to life, you need a solid understanding of Algorithms. I chose the Stanford course taught by Professor Tim Roughgarden and I was pleased with my choice. The professor had a contagious enthusiasm for the subject. The course was rigorous, with most important algorithms proved for correctness and performance (running time and space) in lecture. The programming assignments were well thought out and the Coursera forums were fairly active in the offering I took.

By the end, you will have a solid ability to implement most of the algorithm “Greatest Hits” (a phrase Professor Roughgarden used frequently).

* I also completed the first half of MIT’s 6.006 algorithms course on OCW. I thought this was very good too, with terrific lectures and thoughtful assignments. So, 6.006 is a good alternative if the Stanford course doesn’t suit you.

Learning from Data (CalTech / EdX)

Back to machine learning. I mentioned you would eventually need to take a more rigorous, theoretical course on machine learning. This course the same as the on-campus CalTech course taught by Professor Abu-Mostafa. From the first lecture, it is obvious how much care and thought the professor has put into choosing exactly what to teach and how to teach it.

The course begins, appropriately, by answering the question “Is Learning Feasible?” This question is fundamental to what we are trying to accomplish with machine learning. It is important at some point to address this question head-on. Then, to quote from the course text, “From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know.” The course doesn’t try to cover every learning algorithm or recipe. But rather Professor Abu-Mostafa has carefully chosen what to teach with a clear purpose. When the course ends you are prepared to go off in many directions with a solid foundation.

The course does an in-depth treatment of linear and logistic regression, support vector machines (SVM), neural networks, and clustering. Take the time to read the e-Chapters from the book, they are all very important. Complete the exercises, and take the time to understand the EM algorithm derivation.

* I will mention a 2nd option. Professor Ng’s full CS229 course is available through Stanford Engineering Everywhere. This includes all the assignments and actual on-campus lectures. I have no doubt this course is excellent.

Information Theory, Inference and Learning Algorithms (University of Cambridge)

This course by Professor David MacKay is a gem. I was saddened to find out that Professor MacKay has passed away - much too early. The course consists of the recorded lectures and the accompanying text. It is an advanced course and builds nicely on everything you have learned so far. With this course, you begin to set yourself apart from the crowd.

Professor Mackay takes a unique and inspired approach to teaching machine learning. He begins with a review of probability, entropy, inference and information theory. Chapter 4 closes with a beautiful treatment of Shannon’s Source Coding Theorem. Work every problem presented by the cartoon rat! This will cement several topics you have learned so far.

After Chapter 4, you can proceed directly to Chapters 19-46. These are short, beautifully written chapters. Each is only 12-14 pages including the exercises and solutions. Do all the exercises that have solutions. Note: Not all of these chapters have corresponding lecture videos.

Here are a few things you will understand deeply after completing this course:

  • Bayesian model comparison and the Occam factor
  • Variational methods
  • Monte Carlo methods:
    • Metropolis method, Gibbs sampling, Hamiltonian Monte Carlo, Overrelaxation
  • Hopfield Networks and Boltzmann Machines
  • Addressing high-dimensionality

The return on your effort in this course is very high.

Neural Networks for Machine Learning (University of Toronto / Coursera)

This is an advanced course on Neural Networks taught by Professor Geoff Hinton. There are only a few programming assignments, and by now these will be very easy for you. So, to get the most out of this course you will need to invent a few side projects. Here are some ideas:

  • Implement a Restricted Boltzmann machine
  • Implement a RNN
    • With both gated (e.g. LSTM or GRU) and non-gated cells
  • Experiment with dropout
  • Build a deep net and pre-train layers

I found the book Deep Learning to be an excellent companion to this course. I recommend that you read this book cover-to-cover. Also, read the papers that Professor Hinton has attached to the course material.

Probabilistic Graphical Models (Stanford / Coursera)

This is the only course I was uncertain about recommending. It is an important topic, and this is one of only a few graduate-level MOOCs available. This particular course textbook is difficult to read and the lectures can leave gaps in understanding. There are very few students participating in the forums and no official course TAs or mentors.

All that said, I am very glad I completed it. It forced me to better understand several topics: Markov Networks, variational methods, EM algorithm, and energy-based probability models. You should be better prepared for this course than I was: you will have completed David MacKay’s course.

To make the course worthwhile, you must take it with the “Honors” option. The honors programming assignments are critical to learning the material. They are challenging, with at least one taking me 15 hours to complete.

Your Foundation is Built. What Next?

At this point, you have built the foundation you need to head off in many different directions and excel. From here, you may want to choose a specialization and take a couple of additional courses. Are you more interested in machine learning or AI? The distinction between the two being fuzzy sometimes. Here are some suggestions:

Most importantly, put your knowledge into practice. This is where real learning takes place: solving problems where a professor hasn’t carefully planned your path. Better yet, find a job where you can work with experts in the field. While you are looking for a job, do some challenging projects to highlight your abilities. Document your work and post your code.

This surely seems intimidating, but make forward progress each day and you’ll be there before you know it. I hope you enjoy your journey as much as I have enjoyed mine.



of international television channels  nhkworld mikes ahead on this - many opportunities to linkin japan this year

there are so many opportunities to do this out of boston if only someone actually helped joi ito at media lab




A great guide is daniel bourke
 I began looking into Machine Learning (ML) and Artificial Intelligence (AI).

The computer learns the things for you?” I couldn’t believe it.

I stumbled across Udacity’s Deep Learning Nanodegree. A fun character called Siraj Raval was in one of the promo videos. His energy was contagious. Despite not meeting the basic requirements (I had never written a line of Python before), I signed up.

 I completed the course within the designated timeline. It was hard. Really hard at times. My first two projects were handed in four days late. But the excitement of being involved in one of the most important technologies in the world drove me forward.

Finishing the Deep Learning Nanodegree, I had guaranteed acceptance into either Udacity’s AI Nanodegree, Self-Driving Car Nanodegree or Robotics Nanodegree. All great options.I was a little lost. “Where do I go next?”

I needed a curriculum. I’d built a little foundation with the Deep Learning Nanodegree, now it was time to figure out where I’d head next.


My Self-Created AI Masters Degree

I didn’t plan on going back to university anytime soon. I didn’t have $100,000 for a proper Masters Degree anyway.

So I did what I did in the beginning. Asked my mentor, Google, for help.

I’d jumped into deep learning without any prior knowledge of the field. Instead of climbing to the tip of the AI iceberg, a helicopter had dropped me off on the top.

After researching a bunch of courses, I put a list of which ones interested me the most in Trello.

Trello is my personal assistant/course coordinator.

 

To make myself accountable, I started sharing my learning journey online. I figured I could practice communicating what I learned plus find other people who were interested in the same things I was. I made the Trello board public and wrote a blog post about my endeavours.

The curriculum has changed slightly since I first wrote it but it’s still relevant and I visit the Trello board multiple times per week to track my progress....

I spent the day meeting the Max Kelsen team and the problems they were working on.Two Thursday’s later, Nick, the CEO, Athon, lead machine learning engineer, and I went for coffee.

“How would you like to join the team?” Asked Nick.

“Sure.” I said.Sharing your work

Learning online, I knew it was unconventional. All the roles I’d gone to apply for had Masters Degree requirements or at least some kind of technical degree.I didn’t have either of these. But I did have the skills I’d gathered from a plethora of online courses.Along the way, I was sharing my work online. My GitHub contained all the projects I’d done, my LinkedIn was stacked out and I’d practiced communicating what I learned through YouTube and articles on Medium.My body of work was my resume.

Regardless if you’re learning online or through a Masters Degree, having a portfolio of what you’ve worked on is a great way to build skin in the game.

ML and AI skills are in demand but that doesn’t mean you don’t have to showcase them. Even the best product won’t sell without any shelf space.

Whether it be GitHub, Kaggle, LinkedIn or a blog, have somewhere where people can find you. Plus, having your own corner of the internet is great fun.


How do you start?

Where do you go to learn these skills? What courses are the best?

 

What’s more important than how you start is why you start.

Start with why.Why do you want to learn these skills?

  • Do you want to make money?
  • Do you want to build things?
  • Do you want to make a difference?Got a why? Good. Time for some hard skills.I can only recommend what I’ve tried.

I’ve completed courses from (in order):

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

If you want to apply machine learning and AI techniques to a problem, you don’t necessarily need an in-depth understanding of the math to get a good result. Libraries such as TensorFlow and PyTorch allow someone with a bit of Python experience to build state of the art models whilst the math is taken care of behind the scenes.

If you’re looking to get deep into machine learning and AI research, through means of a PhD program or something similar, having an in-depth knowledge of the math is paramount.

In my case, I’m not looking to dive deep into the math and improve an algorithm’s performance by 10%. I’ll leave that to people smarter than me.

Instead, I’m more than happy to use the libraries available to me and manipulate them to help solve problems as I see fit.


What does a machine learning engineer actually do?

What a machine engineer does in practice might not be what you think.

Despite the cover photos of many online articles, it doesn’t always involve working with robots that have red eyes.

Here are a few questions an ML engineer has to ask themselves daily.

  • Context — How can ML be used to help learn more about your problem?
  • Data — Do you need more data? What form does it need to be in? What do you do when data is missing?
  • Modeling — Which model should you use? Does it work too well on the data (overfitting)? Or why doesn’t it work very well (underfitting)?
  • Production — How can you take your model to production? Should it be an online model or should it be updated at time intervals?
  • Ongoing — What happens if your model breaks? How do you improve it with more data? Is there a better way of doing things?

I borrowed these from a great article by Rachel Thomas, one of the co-founders of fast.ai, she goes into more depth in the full text.

PS if you want have any questions, feel free to reach out to me anytime at mrdbourke.com.

*This article originally appeared as an answer by Daniel Bourke on Quora.




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To: "chris.macrae@yahoo.co.uk" <chris.macrae@yahoo.co.uk>
Sent: Friday, 4 January 2019, 09:06:50 GMT-5
Subject: An entire town goes ‘autism-friendly’
 

HEAD START News and perspectives to begin your day

The Town That Gave the World Spam Is Proud to Be ‘Autism-Friendly’
Austin Minnesota is an autism-friendly town. Ten years ago, it became one of the first in the country to launch a community-wide effort both to reduce the disorder’s stigma and make local businesses aware of the special needs of autistic customers. It is also probably the only small town in America to employ a community autism resource specialist.

Canada Says, ‘Give Me Your MBAs, Your Entrepreneurs’
Canada’s immigration system has long targeted the highly skilled. More than 65 percent of foreign-born adults had a post-secondary degree in 2017, the highest share tracked by members of the Organization for Economic Cooperation and Development. “We are the biggest talent poachers in the OECD,” says Stéfane Marion, chief economist of the National Bank of Canada.


IT’S UP TO US - Be part of the change at CSI

LiisBeth is an fast growing Canadian-based online feminist business magazine and community that serves a growing community of 2000+ edge-walking entrepreneurs worldwide. They are looking for feminist entrepreneurs to take their survey to capture perspectives on feminism. They also want to know how they can help you realize the world of your dreams. In return for your generosity, they will be publishing the results on LiisBeth so that you can also get to know this community better and find out about resources you may have missed!


WHAT'S TONYA READING? - A peek into our CEO's browsing history

Ownership as Social Relation: Nonprofit Strategies to Build Community Wealth through Land. 
Tonya says: "Community Land Trusts provide not only land for housing, but land for working farms, land for manufacturing sites, and land for our main streets to make sure retail sites are affordable for locally owned businesses. In the end, the result would be a diversified and distributed wealth. Solutions, people! Real solutions that work!"


GO FOR IT - Opportunities

Art practiced at a community level creates a powerful sense of inclusion, understanding and the possibility of self-expression among participants. Through its Community Arts Program, Toronto Arts Council provides Project funding to Toronto non-profit organizations and collectives to pursue one-time or time-limited community arts projects. To be eligible for Project funding, applicants must be incorporated non-profit organizations or unincorporated collectives operating on a not-for-profit basis.


DREAM JOB ALERT - Come work in our network

AIDS-Free World is seeking immediate help with its social media accounts. They are looking to contract an experienced professional with an impressive track record in effective online communications and a perspective aligned with our organization's. Their preference is for candidates who are available right away for a short-term contract, working remotely on a 35 hour/week basis, but part-time is also an option. If you qualify, please contact info@aidsfreeworld.org with your CV and a note detailing your interest.


SAVE THE DATE - Upcoming events

Are you an International Engineering ,Technology or Science Grad interested in working on the Environment Sector? OSPE's Skills & Jobs in Ontario's Environment Sector course starts on January 8.

On January 11, Happy Healthy Women is delighted to present a Money Management for Women workshop with Francine Dick, a Certified Financial Planner.

On January 16, Camp Tech wants to help you Build Your Business with Content Marketing. Learn to improve your SEO, increase your sales, attract the best talent, and differentiate from your competitors.

Are you excited to become a B Corp but need the headspace to finish your B impact assessment? On January 22, check out Toronto B Corp Hackathon: Fast Track to Certification.

Join Vina Nadjibulla and Rob Sinclair from Leadership Mastery Inc on January 22 for a dynamic, experiential workshop that will explore the power of collective leadership.


EXCERPT - Words from the world of social innovation

Calgary gives sexual assault victims up to a year to decide to report to the police.

Want to know more about what's new at CSI? Check out our latest stories!

 
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 #TheEconomist #BR6 -AI America


Melinda Gates co-chairs report on pathways to prosperity (2018)

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Melissa co-chairs the UN report in Digital Cooperation 1 2 due March 2019- here's an extract from her Wired conversation with  Fei-Fei Li,: “As an educator, as a woman, as a woman of color, as a mother, I’m increasingly worried,” she says. “AI is about to make the biggest changes to humanity, and we’re missing a whole generation of diverse technologists and leaders.” From the chair next to her, Melinda Gates affirms this, adding, “If we don’t get women and people of color at the table — real technologists doing the real work — we will bias systems. Trying to reverse that a decade or two from now will be so much more difficult, if not close to impossible.” 

 As chief scientist of artificial intelligence and machine learning for Google Cloud, Li is currently on sabbatical from Stanford, where she directs the Stanford Artificial Intelligence Lab. Gates studied artificial intelligence in the early days of the 1980s when when she was learning to code at Duke University. She spent a decade at Microsoft before leaving and later pursuing philanthropy. Now Gates is putting her mind and her money behind a national nonprofit that Li is helping launch: AI4All. 

AI4ALL partners with top universities to educate future AI talent about AI for social good.

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Amandeep Gill @gioasempre 12 Nov 2017 c-chair UN Digital Cooperation

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Alibaba’s DAMO Academy chip industry in China. Today, Alibaba DAMO Academy (DAMO stands for Discovery, Adventure, Momentum, and Outlook) claims that it is developing a neural network chip called Ali-NPU, which will be used in AI reasoning calculations such as video image analysis and machine learning. DAMO Academy says that according to its design, the chip’s cost performance will be 40 times that of existing products.

SEE ALSO: Alibaba Jack Ma’s Promise of 1 Million U.S. Jobs Now in Jeopardy   Jack Ma and Hupan University: Cultivating the Next Generation of Chinese Entreprenuers Through Failure Education

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Singapore's Grab Ends Its Game-Changing Year with $3 Billion in New Funds

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Rideshare Advertising Startup Firefly Launches Following $21.5M Seed Round

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Christine Tsai speaks about her leadership style in an ELLE Magazine cover story.

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 Our Vietnam fund ends an impressive year raising $14.1M from $10M round.

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Partner Neha Khera explains why Toronto tech is an untapped market.

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3rd annual 500 Kobe Accelerator packs the house with over 400 investors for 18 batch company pitches.

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Enjoy these opportunities from Startup of the Year, NASA iTech, and more to help you end the year on a strong note and kick off 2019 in high-gear. 
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The 2019 Startup of the Year application is now open! We are seeking early-stage startups from around the world that have an ingenious product or service.  
 
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Startup of the Year will offer more fast-track opportunities this year, which will range in a variety of events/activities such as Startup Night SXSW in Austin, TX - to regional events that connect us to our Community Partners - to much, much more. Winners of fast-track opportunities will be invited to our big Startup of the Year event.
 
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axios 12/21/18

1. AI and the future of researchThe end of papers

2. Big Tech infightingGoogle and Apple love the Amazon backlash

3. Political AIInventors join the raging public debate

4. Staving off a robot apocalypseWhat might work 

no womder 99% of western professor hate AI

 Ever since science became a formal discipline some five centuries ago, academic research — a fundamental driver of innovation — has, on and off, seemed broken: Scientists have cranked out too many incremental advances, fallen behind on the best research in their field and produced unreplicable work.

Driving the news: Now, some are again rethinking the process, hoping that artificial intelligence could be the long-sought highway to faster and more reliable scientific discovery.

Show less

Why it matters: The U.S. government spends billions on academic research each year — and companies toss in billions more. Yet science can appear to be treading water, turning out a similar scale of breakthroughs as when funding was lower and the number of researchers smaller.

One problem: A combination of factors — higher funding, faster computers and far more data — results in researchers spending much precious time sorting through a relentless avalanche of scholarship.

  • They can't read everything that is out there or attend every conference. It’s easy to miss a solution that’s already borne fruit in another field, or even an adjacent sub-discipline.
  • In order to connect the dots and come up with the best possible research path, they can only hope that they have read the rightarticles or heard the right public speaker.
  • "We need automatic techniques to see what’s missing," said Hannaneh Hajishirzi, an AI expert at the University of Washington.

Language is the core of the problem. Papers are ostensibly written for other scientists to read and understand, but the sheer volume of information means the scientists are in serious need of help.

The answer, some think, is simply to do a better job of sorting, cataloging and assessing papers as they are published.

  • We’ve reported on efforts to monitor social media activity to boost the best papers — but the next step is to engage with the text itself.
  • Several databases already link papers based on citations. Now, some are using natural language processing to extract actual meaning from research — a remarkably difficult task.

A first step is to automatically check facts and compare results against previous work.

  • Scite, a new website that catalogs academic papers, uses machine learning to understand the context in which research is cited. For each paper, Scite lists other work that mentions it neutrally, supports it or contradicts its findings.
  • Josh Nicholson, Scite’s co-founder, says he hopes the system incentivizes greater replication of original findings. When AI highlights corroborating or contradicting research, it should create feedback loops that encourage accuracy and reproducibility, he tells Axios.
  • Companies are also using language understanding in the laborious peer review process that precedes publication, reports Douglas Heaven for Nature.

Between the lines: This is the tip of the arrowhead.

  • Scientists imagine a future where research results are fed into a unified database that is constantly being updated with the latest work.
  • Rather than printing numbers in a table, results would go straight into this database — formatted for computers, not people, to read — and immediately be checked against other researchers’ findings.
"The model of referring to a text-based paper for the purpose of communicating experimental results will probably disappear."
— Robert Murphy, professor of computational biology, Carnegie Mellon

But, but, but: This automated utopia is a long way off. Natural language processing is still hard for computers, and a system trained to understand papers in a particular field might fail when reading another field’s work.

  • Academic journals are still kingmakers, and possessive researchers may not be willing to share their work freely.
  • But some fields’ early stabs at solving intractable research issues have convinced experts like CMU’s Murphy that they won’t exist in 10 years.

Go deeper: AI is helping automate science

Earlier this month, Ed Felten — a Princeton professor and former adviser to President Obama — chided an international audience of artificial intelligence experts packing a cavernous Montreal convention center.

What he's saying: For too long, AI hands have been hiding in their basements, in effect playing God by deciding which technology is ultimately released to the masses, Felten said. Stop assuming that you know what's best for people, he admonished his listeners, and instead dive into the already-raging public debate of what happens next with AI.

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What's happening: As scientists in major fields have done for decades, AI experts are being prodded to step out of the lab, get political and help formulate how society confronts what they are creating.

Why it matters: It's important that the makers of AI are involved in the debate over the ultimate boundaries and uses of technologies that will transform how people live and work in the coming decades and beyond.

"It’s only fair that those whose lives we are going to change should have some say in how that change comes about. Decisions will be made. What is our role?"
— Ed Felten, Princeton professor

What’s happening: Employees at GoogleMicrosoft and Amazon — all of them dominant AI developers — have signed petitions urging their companies to back away from contracts to provide AI software to defense and law enforcement agencies.

But as AI increasingly informs life-altering decisions in banking, defense and other areas, top figures in the field are marshaling researchers for political action. In Montreal for the NeurIPS conference, Felten banged the war drum.

  • "We have a duty to be more active and more constructive in participating in public life," Felten said.
  • He laid out a mathematical model of democracy to explain why political decisions can seem nonsensical. (It's about one-third of the way down in this slideshow.)
  • Felten's bottom line: Fight to be in the room with political deciders, and encourage a culture that engages publicly.

Such momentum is slowly building.

  • "The group of us deeply concerned about the societal impacts of AI has grown extensively," said Brent Hecht, chair of the ACM Future of Computing Academy, an association of young computing professionals.
  • "People in computer science are definitely becoming aware of the impact that their research has on their society," said Mikey Fischer, a Stanford computer science Ph.D. student. 

This movement is being pushed along by nonprofits, including the Partnership on AI and OpenAI. The Center for a New American Security, a think tank, has convened back-room conversations between policymakers and researchers.

Go deeper: Confronting the demons of the computer age

.