Monthly Archives: July 2017

A new week brings a fresh Google initiative targeting AI startups. We started the month with the announcement of Gradient Ventures, Googles on-balance sheet AI investment vehicle. Two days later we watched the finalists of Google Clouds machine learning competition pitch to a panel of top AI investors. And today, Googles Launchpad is announcing a new hands-on Studio program to feed hungry AI startups the resources they need to get off the ground and scale.

The thesis is simple not all startups are created the same. AI startups love data and struggle to get enough of it. They often have to go to market in phases, iterating as new data becomes available. And they typically have highly technical teams and a dearth of product talent. You get the picture.

The Launchpad Studio aims to address these needs head-on with specialized data sets, simulation tools and prototyping assistance. Another selling point of the Launchpad Studio is that startups accepted will have access to Google talent, including engineers, IP experts and product specialists.

Launchpad, to date, operates in 40 countries around the world, explains Roy Geva Glasberg, Googles Global Lead for Accelerator efforts. We have worked with over 10,000 startups and trained over 2,000 mentors globally.

This core mentor base will serve as a recruiting pool for mentors that will assist the Studio.Barak Hachamov, board member for Launchpad, has been traveling around the world withGlasberg to identify new mentors for the program.

The idea of a startup studio isnt new. It has been attempted a handful of times in recent years, but seems to have finally caught on withAndy Rubins Playground Global. Playground offers startups extensive services and access to top talent to dial-in products and compete with the largest of tech companies.

On the AI Studio front, Yoshua Bengios Element AI raised a $102 million Series A to create a similar program. Bengio, one of, if not the, most famous AI researchers, can help attract top machine learning talent to enable recruiting parity with top AI groups like Googles DeepMind and Facebooks FAIR. Launchpad Studio wont have Bengio, but it will bringPeter Norvig, Dan Ariely, Yossi Matias and Chris DiBona to the table.

But unlike Playgrounds $300 million accompanying venture capital arm and Elements own coffers, Launchpad Studio doesnt actually have any capital to deploy. On one hand, capital completes the package. On the other, Ive never heard a good AI startup complain about not being able to raise funding.

Launchpad Studio sits on top of the Google Developer Launchpad network. The group has been operating an accelerator with global scale for some time now. Now on its fourth class of startups, the team has had time to flesh out its vision and build relationships with experts within Google to ease startup woes.

Launchpad has positioned itself as the Google global program for startups, asserts Glasberg. It is the most scaleable tool Google has today to reach, empower, train and support startups globally.

With all the resources in the world, Googles biggest challenge with its Studio wont be vision or execution but this doesnt guarantee everything will be smooth sailing. Between GV, Capital G, Gradient Ventures, GCP and Studio, entrepreneurs are going to have a lot of potential touch-points with the company.

On paper, Launchpad Studio is the Switzerland of Googles programs. It doesnt aim to make money or strengthen Google Clouds positioning. But from the perspective of founders, theres bound to be some confusion. In an ideal world we will see a meeting of the minds between Launchpads Glasberg, Gradients Anna Patterson and GCPs Sam OKeefe.

The Launchpad Studio will be based in San Francisco, with additional operations in Tel Aviv and New York City. Eventually Toronto, London, Bangalore and Singapore will host events locally for AI founders.

Applications to the Studio are now open if youre interested you can apply here.The program itself is stage-agnostic, so there are no restrictions on size. Ideally early and later-stage startups can learn from each other as they scale machine learning models to larger audiences.

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Google launches its own AI Studio to foster machine intelligence startups - TechCrunch

In 2006, Fei-Fei Li started ruminating on an idea.

Li, a newly-minted computer science professor at University of Illinois Urbana-Champaign, saw her colleagues across academia and the AI industry hammering away at the same concept: a better algorithm would make better decisions, regardless of the data.

But she realized a limitation to this approachthe best algorithm wouldnt work well if the data it learned from didnt reflect the real world.

Her solution: build a better dataset.

We decided we wanted to do something that was completely historically unprecedented, Li said, referring to a small team who would initially work with her. Were going to map out the entire world of objects.

The resulting dataset was called ImageNet. Originally published in 2009 as a research poster stuck in the corner of a Miami Beach conference center, the dataset quickly evolved into an annual competition to see which algorithms could identify objects in the datasets images with the lowest error rate. Many see it as the catalyst for the AI boom the world is experiencing today.

Alumni of the ImageNet challenge can be found in every corner of the tech world. The contests first winners in 2010 went on to take senior roles at Baidu, Google, and Huawei. Matthew Zeiler built Clarifai based off his 2013 ImageNet win, and is now backed by $40 million in VC funding. In 2014, Google split the winning title with two researchers from Oxford, who were quickly snapped up and added to its recently-acquired DeepMind lab.

Li herself is now chief scientist at Google Cloud, a professor at Stanford, and director of the universitys AI lab.

Today, shell take the stage at CVPR to talk about ImageNets annual results for the last time2017 was the final year of the competition. In just seven years, the winning accuracy in classifying objects in the dataset rose from 71.8% to 97.3%, surpassing human abilities and effectively proving that bigger data leads to better decisions.

Even as the competition ends, its legacy is already taking shape. Since 2009, dozens of new AI research datasets have been introduced in subfields like computer vision, natural language processing, and voice recognition.

The paradigm shift of the ImageNet thinking is that while a lot of people are paying attention to models, lets pay attention to data, Li said. Data will redefine how we think about models.

In the late 1980s, Princeton psychologist George Miller started a project called WordNet, with the aim of building a hierarchal structure for the English language. It would be sort of like a dictionary, but words would be shown in relation to other words rather than alphabetical order. For example, within WordNet, the word dog would be nested under canine, which would be nested under mammal, and so on. It was a way to organize language that relied on machine-readable logic, and amassed more than 155,000 indexed words.

Li, in her first teaching job at UIUC, had been grappling with one of the core tensions in machine learning: overfitting and generalization. When an algorithm can only work with data thats close to what its seen before, the model is considered overfitting to the data; it cant understand anything more general past those examples. On the other hand, if a model doesnt pick up the right patterns between the data, its overgeneralizing.

Finding the perfect algorithm seemed distant, Li says. She saw that previous datasets didnt capture how variable the world could beeven just identifying pictures of cats is infinitely complex. But by giving the algorithms more examples of how complex the world could be, it made mathematic sense they could fare better. If you only saw five pictures of cats, youd only have five camera angles, lighting conditions, and maybe variety of cat. But if youve seen 500 pictures of cats, there are many more examples to draw commonalities from.

Li started to read about how others had attempted to catalogue a fair representation of the world with data. During that search, she found WordNet.

Having read about WordNets approach, Li met with professor Christiane Fellbaum, a researcher influential in the continued work on WordNet, during a 2006 visit to Princeton. Fellbaum had the idea that WordNet could have an image associated with each of the words, more as a reference rather than a computer vision dataset. Coming from that meeting, Li imagined something grandera large-scale dataset with many examples of each word.

Months later Li joined the Princeton faculty, her alma mater, and started on the ImageNet project in early 2007. She started to build a team to help with the challenge, first recruiting a fellow professor, Kai Li, who then convinced Ph.D student Jia Deng to transfer into Lis lab. Deng has helped run the ImageNet project through 2017.

It was clear to me that this was something that was very different from what other people were doing, were focused on at the time, Deng said. I had a clear idea that this would change how the game was played in vision research, but I didnt know how it would change.

The objects in the dataset would range from concrete objects, like pandas or churches, to abstract ideas like love.

Lis first idea was to hire undergraduate students for $10 an hour to manually find images and add them to the dataset. But back-of-the-napkin math quickly made Li realize that at the undergrads rate of collecting images it would take 90 years to complete.

After the undergrad task force was disbanded, Li and the team went back to the drawing board. What if computer-vision algorithms could pick the photos from the internet, and humans would then just curate the images? But after a few months of tinkering with algorithms, the team came to the conclusion that this technique wasnt sustainable eitherfuture algorithms would be constricted to only judging what algorithms were capable of recognizing at the time the dataset was compiled.

Undergrads were time-consuming, algorithms were flawed, and the team didnt have moneyLi said the project failed to win any of the federal grants she applied for, receiving comments on proposals that it was shameful Princeton would research this topic, and that the only strength of proposal was that Li was a woman.

A solution finally surfaced in a chance hallway conversation with a graduate student who asked Li whether she had heard of Amazon Mechanical Turk, a service where hordes of humans sitting at computers around the world would complete small online tasks for pennies.

He showed me the website, and I can tell you literally that day I knew the ImageNet project was going to happen, she said. Suddenly we found a tool that could scale, that we could not possibly dream of by hiring Princeton undergrads.

Mechanical Turk brought its own slew of hurdles, with much of the work fielded by two of Lis Ph.D students, Jia Deng and Olga Russakovsky . For example, how many Turkers needed to look at each image? Maybe two people could determine that a cat was a cat, but an image of a miniature husky might require 10 rounds of validation. What if some Turkers tried to game or cheat the system? Lis team ended up creating a batch of statistical models for Turkers behaviors to help ensure the dataset only included correct images.

Even after finding Mechanical Turk, the dataset took two and a half years to complete. It consisted of 3.2 million labelled images, separated into 5,247 categories, sorted into 12 subtrees like mammal, vehicle, and furniture.

In 2009, Li and her team published the ImageNet paper with the datasetto little fanfare. Li recalls that CVPR, a leading conference in computer vision research, only allowed a poster, instead of an oral presentation, and the team handed out ImageNet-branded pens to drum up interest. People were skeptical of the basic idea that more data would help them develop better algorithms.

There were comments like If you cant even do one object well, why would you do thousands, or tens of thousands of objects? Deng said.

If data is the new oil, it was still dinosaur bones in 2009.

Later in 2009, at a computer vision conference in Kyoto, a researcher named Alex Berg approached Li to suggest that adding an additional aspect to the contest where algorithms would also have to locate where the pictured object was, not just that it existed. Li countered: Come work with me.

Li, Berg, and Deng authored five papers together based on the dataset, exploring how algorithms would interpret such vast amounts of data. The first paper would become a benchmark for how an algorithm would react to thousands of classes of images, the predecessor to the ImageNet competition.

We realized to democratize this idea we needed to reach out further, Li said, speaking on the first paper.

Li then approached a well-known image recognition competition in Europe called PASCAL VOC, which agreed to collaborate and co-brand their competition with ImageNet. The PASCAL challenge was a well-respected competition and dataset, but representative of the previous method of thinking. The competition only had 20 classes, compared to ImageNets 1,000.

As the competition continued in 2011 and into 2012, it soon became a benchmark for how well image classification algorithms fared against the most complex visual dataset assembled at the time.

But researchers also began to notice something more going on than just a competitiontheir algorithms worked better when they trained using the ImageNet dataset.

The nice surprise was that people who trained their models on ImageNet could use them to jumpstart models for other recognition tasks. Youd start with the ImageNet model and then youd fine-tune it for another task, said Berg. That was a breakthrough both for neural nets and just for recognition in general.

Two years after the first ImageNet competition, in 2012, something even bigger happened. Indeed, if the artificial intelligence boom we see today could be attributed to a single event, it would be the announcement of the 2012 ImageNet challenge results.

Geoffrey Hinton, Ilya Sutskever, and Alex Krizhevsky from the University of Toronto submitted a deep convolutional neural network architecture called AlexNetstill used in research to this daywhich beat the field by a whopping 10.8 percentage point margin, which was 41% better than the next best.

ImageNet couldnt come at a better time for Hinton and his two students. Hinton had been working on artificial neural networks since the 1980s, and while some like Yann LeCun had been able to work the technology into ATM check readers through the influence of Bell Labs, Hintons research hadnt found that kind of home. A few years earlier, research from graphics-card manufacturer Nvidia had made these networks process faster, but still not better than other techniques.

Hinton and his team had demonstrated that their networks could perform smaller tasks on smaller datasets, like handwriting detection, but they needed much more data to be useful in the real world.

It was so clear that if you do a really good on ImageNet, you could solve image recognition, said Sutskever.

Today, these convolutional neural networks are everywhereFacebook, where LeCun is director of AI research, uses them to tag your photos; self-driving cars are using them to detect objects; basically anything that knows whats in a image or video uses them. They can tell whats in an image by finding patterns between pixels on ascending levels of abstraction, using thousands to millions of tiny computations on each level. New images are put through the process to match their patterns to learned patterns. Hinton had been pushing his colleagues to take them seriously for decades, but now he had proof that they could beat other state of the art techniques.

Whats more amazing is that people were able to keep improving it with deep learning, Sutskever said, referring to the method that layers neural networks to allow more complex patterns to be processed, now the most popular favor of artificial intelligence. Deep learning is just the right stuff.

The 2012 ImageNet results sent computer vision researchers scrambling to replicate the process. Matthew Zeiler, an NYU Ph.D student who had studied under Hinton, found out about the ImageNet results and, through the University of Toronto connection, got early access to the paper and code. He started working with Rob Fergus, a NYU professor who had also built a career working on neural networks. The two started to develop their submission for the 2013 challenge, and Zeiler eventually left a Google internship weeks early to focus on the submission.

Zeiler and Fergus won that year, and by 2014 all the high-scoring competitors would be deep neural networks, Li said.

This Imagenet 2012 event was definitely what triggered the big explosion of AI today, Zeiler wrote in an email to Quartz. There were definitely some very promising results in speech recognition shortly before this (again many of them sparked by Toronto), but they didnt take off publicly as much as that ImageNet win did in 2012 and the following years.

Today, many consider ImageNet solvedthe error rate is incredibly low at around 2%. But thats for classification, or identifying which object is in an image. This doesnt mean an algorithm knows the properties of that object, where it comes from, what its used for, who made it, or how it interacts with its surroundings. In short, it doesnt actually understand what its seeing. This is mirrored in speech recognition, and even in much of natural language processing. While our AI today is fantastic at knowing what things are, understanding these objects in the context of the world is next. How AI researchers will get there is still unclear.

While the competition is ending, the ImageNet datasetupdated over the years and now more than 13 million images strongwill live on.

Berg says the team tried to retire the one aspect of the challenge in 2014, but faced pushback from companies including Google and Facebook who liked the centralized benchmark. The industry could point to one number and say, Were this good.

Since 2010 there have been a number of other high-profile datasets introduced by Google, Microsoft, and the Canadian Institute for Advanced Research, as deep learning has proven to require data as vast as what ImageNet provided.

Datasets have become haute. Startup founders and venture capitalists will write Medium posts shouting out the latest datasets, and how their algorithms fared on ImageNet. Internet companies such as Google, Facebook, and Amazon have started creating their own internal datasets, based on the millions of images, voice clips, and text snippets entered and shared on their platforms every day. Even startups are beginning to assemble their own datasetsTwentyBN, an AI company focused on video understanding, used Amazon Mechanical Turk to collect videos of Turkers performing simple hand gestures and actions on video. The company has released two datasets free for academic use, each with more than 100,000 videos.

There is a lot of mushrooming and blossoming of all kinds of datasets, from videos to speech to games to everything, Li said.

Its sometimes taken for granted that these datasets, which are intensive to collect, assemble, and vet, are free. Being open and free to use is an original tenet of ImageNet that will outlive the challenge and likely even the dataset.

In 2016, Google released the Open Images database, containing 9 million images in 6,000 categories. Google recently updated the dataset to include labels for where specific objects were located in each image, a staple of the ImageNet challenge after 2014. London-based DeepMind, bought by Google and spun into its own Alphabet company, recently released its own video dataset of humans performing a variety of actions.

One thing ImageNet changed in the field of AI is suddenly people realized the thankless work of making a dataset was at the core of AI research, Li said. People really recognize the importance the dataset is front and center in the research as much as algorithms.

Correction (July 26): An earlier version of this article misspelled the name of Olga Russakovsky.

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The data that transformed AI researchand possibly the world - Quartz

Executive Summary

Recent advances in AI are best thought of as a drop in the cost of prediction.Prediction is useful because it helps improve decisions. But it isnt the only input into decision-making; the other key input is judgment. Judgmentis the process of determining what the reward to a particular action is in a particular environment.In many cases, especially in the near term, humans will be required to exercise this sort of judgment. Theyll specialize in weighing the costs and benefits of different decisions, and then that judgment will be combined with machine-generated predictions to make decisions. But couldnt AI calculate costs and benefits itself? Yes, but someone would have had to program the AI as to what the appropriate profit measure is. This highlights a particular form of human judgment that we believe will become both more common and more valuable.

With the recent explosion in AI, there has been the understandable concern about its potential impact on human work. Plenty of people have tried to predict which industries and jobs will be most affected, and which skills will be most in demand. (Should you learn to code? Or will AI replace coders too?)

Rather than trying to predict specifics, we suggest an alternative approach. Economic theory suggests that AI will substantially raise the value of human judgment. People who display good judgment will become more valuable, not less. But to understand what good judgment entails and why it will become more valuable, we have to be precise about what we mean.

Recent advances in AI are best thought of as a drop in the cost of prediction. By prediction, we dont just mean the futureprediction is about using data that you have to generate data that you dont have, often by translating large amounts of data into small, manageable amounts. For example, using images divided into parts to detect whether or not the image contains a human face is a classic prediction problem. Economic theory tells us that as the cost of machine prediction falls, machines will do more and more prediction.

Prediction is useful because it helps improve decisions. But it isnt the only input into decision-making; the other key input is judgment. Consider the example of a credit card network deciding whether or not to approve each attempted transaction. They want to allow legitimate transactions and decline fraud. They use AI to predict whether each attempted transaction is fraudulent. If such predictions were perfect, the networks decision process is easy. Decline if and only if fraud exists.

However, even the best AIs make mistakes, and that is unlikely to change anytime soon. The people who have run the credit card networks know from experience that there is a trade-off between detecting every case of fraud and inconveniencing the user. (Have you ever had a card declined when you tried to use it while traveling?) And since convenience is the whole credit card business, that trade-off is not something to ignore.

This means that to decide whether to approve a transaction, the credit card network has to know the cost of mistakes. How bad would it be to decline a legitimate transaction? How bad would it be to allow a fraudulent transaction?

Someone at the credit card association needs to assess how the entire organization is affected when a legitimate transaction is denied. They need to trade that off against the effects of allowing a transaction that is fraudulent. And that trade-off may be different for high net worth individuals than for casual card users. No AI can make that call. Humans need to do so.This decision is what we call judgment.

Judgment is the process of determining what the reward to a particular action is in a particular environment. Judgment is howwe work out the benefits and costs of different decisions in different situations.

Credit card fraud is an easy decision to explain in this regard. Judgment involves determining how much money is lost in a fraudulent transaction, how unhappy a legitimate customer will be when a transaction is declined, as well as the reward for doing the right thing and allowing good transactions and declining bad ones. In many other situations, the trade-offs are more complex, and the payoffs are not straightforward. Humans learn the payoffs to different outcomes by experience, making choices and observing their mistakes.

Getting the payoffs right is hard. It requires an understanding of what your organization cares about most, what it benefits from, and what could go wrong.

In many cases, especially in the near term, humans will be required to exercise this sort of judgment. Theyll specialize in weighing the costs and benefits of different decisions, and then that judgment will be combined with machine-generated predictions to make decisions.

But couldnt AI calculate costs and benefits itself? In the credit card example, couldnt AI use customer data to consider the trade-off and optimize for profit? Yes, but someone would have had to program the AI as to what the appropriate profit measure is. This highlights a particular form of human judgment that we believe will become both more common and more valuable.

Like people, AIs can also learn from experience. One important technique in AI is reinforcement learning whereby a computer is trained to take actions that maximize a certain reward function. For instance, DeepMinds AlphaGo was trained this way to maximize its chances of winning the game of Go. Games are often easy to apply this method of learning because the reward can be easily described and programmed shutting out a human from the loop.

But games can be cheated. As Wired reports, when AI researchers trained an AI to play the boat racing game, CoastRunners, the AI figured out how to maximize its score by going around in circles rather than completing the course as was intended. One might consider this ingenuity of a type, but when it comes to applications beyond games this sort of ingenuity can lead to perverse outcomes.

The key point from the CoastRunners example is that in most applications, the goal given to the AI differs from the true and difficult-to-measure objective of the organization. As long as that is the case, humans will play a central role in judgment, and therefore in organizational decision-making.

In fact, even if an organization is enabling AI to make certain decisions, getting the payoffs right for the organization as a whole requires an understanding of how the machines make those decisions. What types of prediction mistakes are likely? How might a machine learn the wrong message?

Enter Reward Function Engineering. As AIs serve up better and cheaper predictions, there is a need to think clearly and work out how to best use those predictions. Reward Function Engineering is the job of determining the rewards to various actions, given the predictions made by the AI. Being great at itrequires having an understanding of the needs of the organization and the capabilities of the machine. (And it is not the same as putting a human in the loop to help train the AI.)

Sometimes Reward Function Engineering involves programming the rewards in advance of the predictions so that actions can be automated. Self-driving vehicles are an example of such hard-coded rewards. Once the prediction is made, the action is instant. But as the CoastRunners example illustrates, getting the reward right isnt trivial. Reward Function Engineering has to consider the possibility that the AI will over-optimize on one metric of success, and in doing so act in a way thats inconsistent with the organizations broader goals.

At other times, such hard-coding of the rewards is too difficult. There may so be many possible predictions that it is too costly for anyone to judge all the possible payoffs in advance. Instead, some human needs to wait for the prediction to arrive, and then assess the payoff. This is closer to how most decision-making works today, whether or not it includes machine-generated predictions. Most of us already do some Reward Function Engineering, but for humans not machines. Parents teach their children values. Mentors teach new workers how the system operates. Managers give objectives to their staff, and then tweak them to get better performance. Every day, we make decisions and judge the rewards. But when we do this for humans, prediction and judgment are grouped together, and the distinct role of Reward Function Engineering has not needed to be explicitly separate.

As machines get better at prediction, the distinct value of Reward Function Engineering will increase as the application of human judgment becomes central.

Overall, will machine prediction decrease or increase the amount of work available for humans in decision-making? It is too early to tell. On the one hand, machine prediction will substitute for human prediction in decision-making. On the other hand, machine prediction is a complement to human judgment. And cheaper prediction will generate more demand for decision-making, so there will be more opportunities to exercise human judgment. So, although it is too early to speculate on the overall impact on jobs, there is little doubt that we will soon be witness to a great flourishing of demand for human judgment in the form of Reward Function Engineering.

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How AI Will Change the Way We Make Decisions - Harvard Business Review

Artificial intelligence-focused investment funds are a dime a dozen these days. Everyone knows theres money to be made from AI, but to capture value, good VCs know they need to back products and not technologies. This has left a bit of a void in the space where research occurs within research institutions and large tech companies and commercialization occurs within verticalized startups there isnt much left for the DIY AI enthusiast. AI Grant, created by Nat Friedman and Daniel Gross, aims to bankroll science projects for the heck of it to give untraditional candidates a shot at solving big problems.

Gross, a partner at Y Combinator, and Friedman, a founder who grewXamarin to acquisition by Microsoft, started working on AI Grant back in April. AI Grant issues no-strings-attached grants to people passionate about interesting AI problems. The more formalized version launching today brings a slate of corporate partners and a more structured application review process.

Anyone, regardless of background, can submit an application for a grant. The application is online and consists of questions about background and prior projects in addition to basic information about what the money will be used for and what the initial steps will be for the project. Applicants are asked to connect their GitHub, LinkedIn, Facebook and Twitter accounts.

Gross told me in an interview that the goal is to build profiles of non-traditional machine learning engineers. Eventually, the data collected from the grant program could allow the two to play a bit of machine learning moneyball valuing machine learning engineers without traditional metrics (like having a PhD from Stanford). You can imagine how all the social data could even help build a model for ideal grant recipients in the future.

The long-term goal is to create a decentralized AI research lab think DeepMind but run through Slack and full of engineers that dont cost $300,000 a pop. One day, the MacArthur genius grant-inspired program could serve other industries outside of AI offering a playground of sorts for the obsessed to build, uninhibited.

The entire AI Grant project reminds me of a cross between a Thiel Fellowship and a Kaggle competition. The former, a program to give smart college dropouts money and freedom to tinker and the later, an innovative platform for evaluating data scientists through competition. Neither strive to advance the field in the way the AI Grant program does, but you can see the ideological similarity around democratizing innovation.

Some of the early proposals to receive the AI Grant include:

Charles River Ventures (CRV) is providing the $2,500 grants that will be handed out to the next 20 fellows. In addition, Google has signed on to provide $20,000 in cloud computing credits to each winner, CrowdFlower is offering $18,000 in platform credit with $5,000 in human labeling credits, Scale is giving $1,000 in human labeling credit per winner and Floyd will give 250 Tesla K80 GPU hours to each winner.

During the first selection of grant winners, Floodgate awarded $5,000 checks. The program launching today will award $2,500 checks. Gross told me that this change was intentional the initial check size was too big. The plan is to add additional flexibility in the future to allow applicants to make a case for how much money they actually need.

You can check out the application here and give it a go. Applications will be taken until August 25th. Final selection of fellows will occur on September 24th.

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AI Grant aims to fund the unfundable to advance AI and solve hard ... - TechCrunch

Human: Oh sweet bot, tell us a story! A nice story! About a very wise human who worked his whole life to save everybody in the world from having to spend time manually tagging their friends in digital photos and made a magic machine that did it for them instead!

Bot: Thats not really a very nice story when you think about it.

Human: Well, tell us about the wise human who thought no-one should ever feel forgotten on their birthday so he made a clever algorithm that always knew to remind the forgetful humans to write happy messages so their friends would never feel sad. He even thought that in future the clever algorithm could suggest what message to write so humans wouldnt even have to think of something nice to tell their friends!

Bot: I feel quite sad after reading that.

Human: And he made another magical algorithm that reminds people of Special Moments in their life even years and years afterwards, in case theyve forgotten that holiday they went on with their ex eight years ago.

Bot: You do realize some people voluntarily medicate themselves with alcohol * * in order * * to forget???

Human: But the wise human also wanted to make sure all humans in the world always felt there was something they needed to read and so he made a special series of algorithms that watched very closely what each human read and looked at and liked and clicked on in order to order the information they saw in such a way that a person never felt they had reached the end of all the familiar things they could click on and could just keep clicking the whole day and night and be reading all the things that felt so very familiar to them so they always felt the same every day and felt they were surrounded by people who felt exactly like them and could just keep on keeping on right as they were each and every day.

Bot: Thats confusing.

Human: And the great humans algorithms became so good at ordering the information which each human wanted to read that other mercenary humans came to realize they could make lots of money by writing fairy stories and feeding them into the machine like how politicians ate little children for breakfast and wore devils horns on Sundays.

Bot: Okay, youre scaring me now

Human: And in the latter years the great human realized it was better to replace all the human writers he had employed to help train the machine how to intelligently order information for humans because it was shown that humans could not be trusted not to be biased.

Bot: Um

Human: After all, the great human had proven years ago that his great machine was capable of manipulating the emotions of the humans that used it. All he needed to do was tweak the algorithmic recipe that determined what each human saw and he could make a person feel great joy or cast them down into a deep pit of despair.

Bot: Help.

Human: The problem was other humans started to notice the machines great power, and became jealous of the great and clever human who wielded this power and dark forces started to move against the great man and his machine.

Bot: Are you talking about regulators?

Human: There were even calls for the man to take editorial responsibility for the output of the machine. The man tried to tell the silly humans that a machine cant be an editor! Only a human can do that! The machine was just a machine! Even if nearly two billion humans were reading what the machine was ordering them to read every single month.

But it was no good. The great human finally realized the machines power was now so great there was no hiding it. So he took up his pen and started writing open letters about the Great Power and Potential of the machine. And all the Good it could do Humanity. All the while telling himself that only when humans truly learned to love the machine would they finally be free to just be themselves.

Humans had to let themselves subconsciously be shown the path of what to click and what to like and who to be friends with. Only then would they be free of the pain and suffering of having nothing to else to click on. And only his great all-seeing algorithm could show them the way, surreptitiously, to that true happiness.

It wasnt something that regulators were capable of understanding. It required he realized real faith in the algorithm.

Bot: Ive heard this story before, frankly, and I know where it ends.

Human: But even the great human knew the limits of his own creation. And selling positive stories about the machines powers was definitely not a job for the machine. So he fired off another email to his subordinates, ordering the (still) human-staffed PR department to add one more human head to its tally, with a special focus on the algorithms powering the machine thinking, as he did so, multiple steps ahead to the great day when such a ridiculous job would no longer be necessary.

Because everyone would love the machine as much as he did.

Bot: Oh I seeeee! Job title: AI Editor Hmm Develop and execute on editorial strategy and campaigns focused on advancements in AI being driven by Facebook. Minimum qualifications: Bachelors degree in English, Journalism, Communications, or related field well chatbots are related to language so I reckon I can make that fly. What else? 8+ years professional communications experience: journalism, agency or in-house. Well Ill need to ingest a media law course or two but I reckonIll challenge myself to apply.

In truth Ive done worse jobs. An AI bots gotta do what an AI bots gotta do, right? Just dont tell an algorithm to be accountable. Ive done my time learning. If theres a problem its not me, its the data, okay? Okay?

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Facebook is hiring a (human) AI Editor | TechCrunch - TechCrunch

Hot on the heels of reports that Facebook is developing its own take on Amazon Echo, Chinas Xiaomi has joined the tech company masses by jumping into the increasingly crowded smart speaker space.

The Mi AI Speaker is Xiaomis first take at rivaling the Echo, which has alreadyinspired a product from Alibaba in China and countsofferings from Googleand Appleamong its competitors.

Building on the voice-controlled speaker that Xiaomi shipped in December, the new device is powered by artificial intelligence, the company said, which has just been added the Xiaomis MIUI operating system, a variant of Android. The speaker can be used as a control for Xiaomi products and also over 30 smart products from Xiaomis partners. Xiaomi touted its content available for the speaker, which includes music, audio books, kids stories and radio.

In terms of audio itself, the device uses a setup of six microphones for 360 degree sound broadcast.

The price will be 299 RMB, $45, when it goes on sale in August but the usual caveat applies.As is often the case with Xiaomi products, the initial release is confirmed for China but we dont have word of international availability.

Early bird users in China can pick up a Mi AI Speaker for almost free just 1RMB in a working-beta test that Xiaomi says will improve the AI systems andhelp train [it] to be even more intelligent in the early stage.

The speaker was unveiled at an event in Beijing today where Xiaomi took the wraps of MIUI 9, which includes a bevy of AI-powered features such as a digital assistant and quick app launch capabilities.

The company also launched the Mi5X smartphone, a5.5-inch device that ships with MIUI9 and features a dual rear camera. The phone is priced from 1499 RMB, or $220.

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Xiaomi's take on the Amazon Echo smart speaker costs less than $50 - TechCrunch