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Main article: Artificial intelligence

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Why AWS is selling a MIDI keyboard to teach machine learning

03:00 | 6 December

Earlier this week, AWS launched DeepComposer, a set of web-based tools for learning about AI to make music and a $99 MIDI keyboard for inputting melodies. That launch created a fair bit of confusion, though, so we sat down with Mike Miller, the director of AWS’s AI Devices group, to talk about where DeepComposer fits into the company’s lineup of AI devices, which includes the DeepLens camera and the DeepRacer AI car, both of which are meant to teach developers about specific AI concepts, too.

The first thing that’s important to remember here is that DeepComposer is a learning tool. It’s not meant for musicians — it’s meant for engineers who want to learn about generative AI. But AWS didn’t help itself by calling this “the world’s first machine learning-enabled musical keyboard for developers.” The keyboard itself, after all, is just a standard, basic MIDI keyboard. There’s no intelligence in it. All of the AI work is happening in the cloud.

“The goal here is to teach generative AI as one of the most interesting trends in machine learning in the last 10 years,” Miller told us. “We specifically told GANs, generative adversarial networks, where there are two networks that are trained together. The reason that’s interesting from our perspective for developers is that it’s very complicated and a lot of the things that developers learn about training machine learning models get jumbled up when you’re training two together.”

With DeepComposer, the developer steps through a process of learning the basics. With the keyboard, you can input a basic melody — but if you don’t have it, you also can use an on-screen keyboard to get started or use a few default melodies (think Ode to Joy). From a practical perspective, the system then goes out and generates a background track for that melody based on a musical style you choose. To keep things simple, the system ignores some values from the keyboard, though, including velocity (just in case you needed more evidence that this is not a keyboard for musicians). But more importantly, developers can then also dig into the actual models the system generated — and even export them to a Jupyter notebook.

For the purpose of DeepComposer, the MIDI data is just another data source to teach developers about GANs and SageMaker, AWS’s machine learning platform that powers DeepComposer behind the scenes.

“The advantage of using MIDI files and basing out training on MIDI is that the representation of the data that goes into the training is in a format that is actually the same representation of data in an image, for example,” explained Miller. “And so it’s actually very applicable and analogous, so as a developer look at that SageMaker notebook and understands the data formatting and how we pass the data in, that’s applicable to other domains as well.”

That’s why the tools expose all of the raw data, too, including loss functions, analytics and the results of the various models as they try to get to an acceptable result, etc. Because this is obviously a tool for generating music, it’ll also expose some of the data about the music, like pitch and empty bars.

“We believe that as developers get into the SageMaker models, they’ll see that, hey, I can apply this to other domains and I can take this and make it my own and see what I can generate,” said Miller.

Having heard the results so far, I think it’s safe to say that DeepComposer won’t produce any hits soon. It seems pretty good at creating a drum track, but bass lines seem a bit erratic. Still, it’s a cool demo of this machine learning technique, even though my guess is that its success will be a bit more limited than DeepRacer, which is a concept that is a bit easier to understand for most since the majority of developers will look at it, think they need to be able to play an instrument to use it, and move on.

Additional reporting by Ron Miller.

 


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New tweet generator mocks venture capitalists

23:50 | 5 December

“Airbnb’s unit economics are quite legendary — the S-1 is going to be MOST disrupted FASTEST in the next 3 YEARS? Caps for effect.”

Who Tweeted that? Initialized Capital’s Garry Tan? Homebrew’s Hunter Walk? Y Combinator co-founder Paul Graham? Or perhaps one of the dozens of other venture capitalists active on Twitter .

No, it was Parrot.VC, a new Twitter account and website dedicated to making light of VC Twitter. The creator of the new tool, which first landed on Twitter in late November, fed 65,000 tweets written by some 50 venture capitalists to a machine learning bot. The result is an automated tweet generator ready to spew somewhat nonsensical (or entirely nonsensical) <280-character statements.

[gallery ids="1920794,1920795,1920797,1920806"]

According to Hacker News, where the creator shared information about their project, the bot uses predictive text to generate “amazing, new startup advice,” adding “Gavin Belson – hit me up, this is the perfect acquisition for Hooli,” referencing the popular satirical TV show, Silicon Valley. 

This isn’t the first time someone has leveraged artificial intelligence to make fun of the tech community. One of my personal favorites, BodegaBot, inspired by the Bodega fiasco of late 2017, satirizes Silicon Valley’s unhinged desire to replace domestic service with technology.

 


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AI-enabled assistant robot returning to the Space Station with improved emotional intelligence

21:32 | 5 December

The Crew Interactive Mobile Companion (or CIMON for short) recorded a number of firsts on its initial mission to the International Space Station, which took place last November, including becoming the first ever autonomous free-floating robot to operate aboard the station, and the first ever smart astronaut assistant. But CIMON is much more than an Alexa for space, and CIMON-2, which launched aboard today’s SpaceX ISS resupply mission, will demonstrate a number of ways the astronaut support robot can help those working in space – from both a practical and an emotional angle.

CIMON is the joint-product of a collaboration between IBM, the German Aerospace Center (DLR) and Airbus, and its aim is to design and develop a robotic assistant for use in space that can serve a number of functions, including things as mundane as helping to retrieve information and keep track of tasks astronauts are doing on board the station, and as wild as potentially helping to alleviate or curb the effects of social issues that might arise from settings in which a small team works in close quarters over a long period.

“The goal of mission one was to really to commission CIMON and to really understand if he can actually work with the astronauts – if there are experiments that he can support,” explained IBM’s Matthias Biniok, project manager on the Watson AI aspects of the mission. “So that was very successful – the astronauts really liked working with CIMON.”

“Now, we are looking at the next version: CIMON-2.” Biniok continued. “That has more capability. For example, it has better software and better hardware that has been improved based on the outcomes that we had with mission one – and we have also some new features. So for example, on the artificial intelligence side, we have something called emotional intelligence, based on our IBM Watson Tone Analyzer, with we’re trying to understand and analyze the emotions during a conversation between CIMON and the astronauts to see how they’re feeling – if they’re feeling joyful, if something makes them angry, and so on.”

That, Biniok says, could help evolve CIMON into a robotic countermeasure for something called ‘groupthink,’ a phenomenon wherein a group of people who work closely together gradually have all their opinions migrate towards consensus or similarity. A CIMON with proper emotional intelligence could detect when this might be occurring, and react by either providing an objective, neutral view – or even potentially taking on a contrarian or ‘Devil’s advocate’ perspective, Biniok says.

That’s a a future aim, but in the near-term CIMON can have a lot of practical benefit simply by freeing up time spent on certain tasks by astronauts themselves.

“Time is super expensive on the International Space Station,” Biniok said. “And it’s very limited, so if we could save some crew time with planning that would be super helpful to the astronauts. CIMON can also support experiments – imagine that you’re an astronaut up there, you have complex research experiments going on, and there’s a huge amount of documentation for that. And if you are missing some information, or you have a question about it, then you have to look up in this documentation, and that that takes time. Instead of doing that, so you could actually just ask CIMON – so for example ‘what’s the next step CIMON?, or ‘why am I using Teflon and not any other materials?’

CIMON can also act as a mobile documentarian, using its onboard video camera to record experiments and other activities on the Space Station. It’s able to do so autonomously, too, Biniok notes, so that an astronaut can theoretically ask it to navigate to a specific location, take a photo, then return and show that photo to the astronaut.

This time around, CIMON will be looking to stay on the ISS for a much longer span than version one; up to three years, in fact. Biniok had nothing specific to share on plans beyond that, but did say that long-term, the plan is absolutely to extend CIMON’s mission to include the Moon, Mars and beyond.

 


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Dataiku is now worth $1.4 million following secondary round

18:37 | 4 December

Enterprise AI company Dataiku has announced some changes in its capitalization table. CapitalG (formerly Google Capital), Alphabet’s growth equity investment fund, is investing in the startup by buying out some of Serena Capital’s shares.

Serena Capital has been an investor in Dataiku since 2014. “Serena was looking for a bit of liquidity. They invested in Dataiku when the valuation was 100 times less than what it is following today’s transaction,” Dataiku co-founder and CEO Florian Douetteau told me.

Serena is still keeping a stake in Dataiku and a board seat. CapitalG has acquired those secondary market shares at a valuation of $1.4 billion.

Dataiku helps enterprise clients turn large data sets into actionable insights using machine learning. The company connects to various storage systems and databases, such as Hadoop, NoSQL or image sets.

You can then use Dataiku to clean your data set, create segments and build a machine learning model. The company then lets you run your own model.

Dataiku has always been focused on bringing data science to more people — not just data scientists. If you’re a business analyst, you can take advantage of Dataiku’s visual coding tool to get insights from your data sets.

The company has been switching to a Kubernetes-powered infrastructure, which lets you scale up your Dataiku infrastructure by spinning up more containers and scale it down when you’re done.

Dataiku now generates half of its revenue in the U.S. Clients include big enterprise clients, such as General Electric, Sephora and Unilever.

“We’re very product-centric. We want to handle the data science cycle from start to finish with a single product,” Douetteau said. “This strategy alone makes us stand out.”

 


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This 16-game arcade for AIs tests their playing prowess

03:24 | 4 December

Figuring out just what an AI is good at is one of the hardest thing about understanding them. To help determine this, OpenAI has designed a set of games that can help researchers tell whether their machine learning agent is actually learning basic skills or, what is equally likely, has figured out how to rig the system in its favor.

It’s one of those aspects of AI research that never fails to delight: the ways an agent will bend or break the rules in its endeavors to appear good at whatever the researchers are asking it to do. Cheating may be thinking outside the box, but it isn’t always welcome, and one way to check is to change the rules a bit and see if the system breaks down.

What the agent actually learned can be determined by seeing if those “skills” can be applied when it’s put into new circumstances where only some of its knowledge is relevant.

For instance, say you want to learn if an AI has learned to play a Mario-like game where it travels right and jumps over obstacles. You could switch things around so it has to walk left; you could change the order of the obstacles; or you could change the game entirely and have monsters appear that the AI has to shoot while it travels right instead.

If the agent has really learned something about playing a game like this, it should be able to pick up the modified versions of the game much quicker than something entirely new. This is called “generalizing” — applying existing knowledge to a new set of circumstances — and humans do it constantly.

OpenAI researchers have encountered this many times in their research, and in order to test generalizable AI knowledge at a basic level, they’ve designed a sort of AI arcade where an agent has to prove its mettle in a variety of games with varying overlap of gameplay concepts.

The 16 game environments they designed are similar to games we know and love, like Pac-Man, Super Mario Bros., Asteroids, and so on. The difference is the environments have been build from the ground up towards AI play, with simplified controls, rewards, and graphics.

Each taxes an AI’s abilities in a different way. For instance in one game there may be no penalty for sitting still and observing the game environment for a few seconds, while in others it may place the agent in danger. In some the AI must explore the environment, in others it may be focused on a single big boss spaceship. But they’re all made to be unmistakably different games, not unlike (though obviously a bit different from) what you might find available for an Atari or NES console.

Here’s the full list, as seen in the gif below from top to bottom, left to right:

  • Ninja: Climb a tower while avoiding bombs or destroying them with throwing stars.
  • Coinrun: Get the coin at the right side of the level while avoiding traps and monsters.
  • Plunder: Fire cannonballs from the bottom of the screen to hit enemy ships and avoid friendlies.
  • Caveflyer: Navigate caves using Asteroids-style controls, shooting enemies and avoiding obstacles.
  • Jumper: Open-world platformer with a double-jumping rabbit and compass pointing towards the goal.
  • Miner: Dig through dirt to get diamonds and boulders that obey Atari-era gravity rules.
  • Maze: Navigate randomly generated mazes of various sizes.
  • Bigfish: Eat smaller fish than you to become the bigger fish, while avoiding a similar fate.
  • Chaser: Like Pac-Man, eat the dots and use power pellets strategically to eat enemies.
  • Starpilot: Gradius-like shmup focused on dodging and quick elimination of enemy ships.
  • Bossfight: 1 on 1 battle with a boss ship with randomly selected attacks and replenishing shields.
  • Heist: Navigate a maze with colored locks and corresponding keys.
  • Fruitbot: Ascend through levels while collecting fruit and avoiding non-fruit.
  • Dodgeball: Move around a room without touching walls, hitting others with balls and avoiding getting hit.
  • Climber: Climb a series of platforms collecting stars along the way and avoiding monsters.
  • Leaper: Frogger-type lane-crossing game with cars, logs, etc.

You can imagine that an AI might be created that excels at the grid-based ones like Heist, Maze, and Chaser, but loses the track in Jumper, Coinrun, and Bossfight. Just like a human — because there are different skills involved in each. But there are shared ones as well: understanding that the player character and moving objects may have consequences, or that certain areas of the play area are inaccessible. An AI that can generalize and adapt quickly will learn to dominate all these games in a shorter time than one that doesn’t generalize well.

The set of games and methods for observing and rating agent performance in them is called the ProcGen benchmark, since the environments and enemy placements in the games are procedurally generated. You can read more about them, or learn to build your own little AI arcade, at the project’s GitHub page.

 


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AWS announces new enterprise search tool powered by machine learning

22:44 | 3 December

Today at AWS re:Invent in Las Vegas, the company announced a new search tool called Kendra, which provides natural language search across a variety of content repositories using machine learning.

Matt Wood, AWS VP of artificial intelligence said that the new search tool uses machine learning, but doesn’t actually require machine learning expertise of any kind. Amazon is taking care of that for customers under the hood.

You start by identifying your content repositories. This could be anything from and S3 storage repository to OneDrive to Salesforce — anywhere you store content. You can use pre-built connectors from AWS, provide your credentials, and connect to all of these different tools.

Kendra then builds an index based on the content it finds in the connected repositories, and users can begin to interact with the search tool using natural language queries. The tool understands concepts like time, so if the question is something like ‘When the IT Help Desk is open,’ the search engine understands that this is about time, checks the index and delivers the right information to the user.

The beauty of this search tool is not only that it uses machine learning, but based on simple feedback from a user, like a smiley face or sad face emoji, it can learn which answers are good and which ones require improvement, and it does this automatically for the search team.

Once you have it set up, you can drop the search on your company intranet or you can use it internally inside an applications and it behaves as you would expect a search tool to do with features like type ahead.

 


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AWS announces AutoPilot, more visible AutoML in SageMake Studio

22:12 | 3 December

Today at AWS re:Invent in Las Vegas, the company announced AutoPilot, a new tool that gives you greater visibility into automated machine learning model creation, known as AutoML. This new tool is part of the new SageMaker Studio also announced today.

As AWS CEO Andy Jassy pointed out on stage today, one of the problems with AutoML is that it’s basically a black box. If you want to improve a mediocre model, or just evolve it for your business, you you have no idea how it was built.

The idea behind AutoPilot is to give you the ease of model creation you get from an AutoML-generated model, but also giving you much deeper insight into how the system built the model. “AutoPilot is a way to create a model automatically, but give you full visibility and control,” Jassy said.

“Using a single API call, or a few clicks in Amazon SageMaker Studio, SageMaker Autopilot first inspects your data set, and runs a number of candidates to figure out the optimal combination of data preprocessing steps, machine learning algorithms and hyperparameters. Then, it uses this combination to train an Inference Pipeline, which you can easily deploy either on a real-time endpoint or for batch processing. As usual with Amazon SageMaker, all of this takes place on fully-managed infrastructure,” the company explained in a blog post announcing the new feature.

You can look at the model’s parameters, and see 50 automated models, and it provides you with a leader board of what models performed the best. What’s more, you can look at the model’s underlying notebook, and also see what trade-offs were made to generate that best model. For instance, it may be the most accurate, but sacrifices speed to get that.

Your company may have its own set of unique requirements and you can choose the best model based on whatever parameters you consider to be most important, even though it was generated in an automated fashion.

Once you have the model, you like best, you can go into SageMaker Studio, select it and launch it with a single click. The tool is available now.

 


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Amazon Robotics head Tye Brady will be speaking at TC Sessions Robotics+AI 2020 at UC Berkeley

20:15 | 3 December

We’re gearing up for another great TC Sessions Robotics+AI March 3 at UC Berkeley, and we’ve got some big names to announce. Last week, it was AI expert Stuart Russell and today we’re pleased to note that we’ll be joined by Amazon Robotics Chief Technologist, Tye Brady.

A co-founder of MassRobotics, Brady has held a number of high profile positions throughout the robotics and aerospace industries, including positions at Draper Laboratory, the Massachusetts Autonomous Air Vehicle Research and Innovation Consortium and IEEE. It’s at Amazon, however, that he’s had his largest impact on the future of robotics and retail.

Founded in 2012 with the acquisition of Kiva Systems, Amazon Robotics leverages fulfillment center robots to transform warehouses across the nation. Amazon has amassed one of the world’s largest robotic workforces, with more than 100,000 deployed across various warehouses across the U.S. The system all present unique potential to help streamline the company’s massive number of packages.

During Brady’s tenure, the robotics have become an increasingly essential element of Amazon’s day-to-day operations, working alongside human counterparts to increase the efficiency of the company’s already speedy services. The executive will join us to discuss the company’s efforts and the future of the automation-driven workforce.

Join our 4th annual TC Sessions: Robotics & AI on March 3 at UC Berkeley’s Zellerbach Hall for a remarkable day with the world’s top roboticists, investors, founders, and AI engineers. The day features a full day or programming lead by TechCrunch’s editors on the main stage, a pitch-off competition featuring early-stage startups, numerous breakout and speaker Q&A sessions, and much more.

Get your early bird pass here and save $100 before prices go up. Interested in sponsoring? Please get in touch.

 


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Credit startup Migo expands to Brazil on $20M raise and Africa growth

12:00 | 3 December

After growing its lending business in West Africa, emerging markets credit startup Migo is expanding to Brazil on a $20 million Series B funding round led by Valor Group Capital.

The San Mateo based company — previously branded Mines.io — provides AI driven products to large firms so those companies can extend credit to underbanked consumers in viable ways.

That generally means making lending services to low-income populations in emerging markets profitable for big corporates, where they previously were not.

Founded in 2013, Migo launched in Nigeria, where the startup now counts fintech unicorn Interswitch and Africa’s largest telecom, MTN, among its clients.

Offering its branded products through partner channels, Migo has originated over 3 million loans to over 1 million customers in Nigeria since 2017, according to company stats.

“The global social inequality challenge is driven by a lack of access to credit. If you look at the middle class in developed countries, it is largely built on access to credit,” Migo founder and CEO Ekechi Nwokah told TechCrunch.

“What we are trying to do is to make prosperity available to all by reinventing the way people access and use credit,” he explained.

Migo does this through its cloud-based, data-driven platform to help banks, companies, and telcos make credit decisions around populations they previously may have bypassed.

These entities integrate Migo’s API into their apps to offer these overlooked market segments digital accounts and lines of credit, Nwokah explained.

“Many people are trying to do this with small micro-loans. That’s the first place you understand risk, but we’re developing into point of sale solutions,” he said.

Migo’s client consumers can access their credit-lines and make payments by entering a merchant phone number on their phone (via USSD) and then clicking on “Pay with Migo”. Migo can also be set up for use with QR codes, according to Nwokah.

He believes structural factors in frontier and emerging markets make it difficult for large institutions to serve people without traditional credit profiles.

“What makes it hard for the banks is its just too expensive,” he said of establishing the infrastructure, technology, and staff to serve these market segments.

Nwokah sees similarities in unbanked and underbanked populations across the world, including Brazil and African countries such as Nigeria.

“Statistically, the number of people without credit in Nigeria is about 90 million people and its about 100 million adults that don’t have access to credit in Brazil. The countries are roughly the same size and the problem is roughly the same,” he said.

On clients in Brazil, Migo has a number of deals in the pipeline — according to Nwokah — and has signed a deal with a big-name partner in the South American country of 290 million, but could not yet disclose which one.

Migo generates revenue through interest and fees on its products. With lead investor Valor Group Capital, new investors Africinvest and Cathay Innovation joined existing backers Velocity Capital and The Rise Fund on the startup’s $20 million Series B.

Increasingly, Africa — with its large share of the world’s unbanked — and Nigeria — home to the continent’s largest economy and population — have become proving grounds for startups looking to create scalable emerging market finance solutions.

Migo could become a pioneer of sorts by shaping a fintech credit product in Africa with application in frontier, emerging, and developed markets.

“We could actually take this to the U.S. We’ve had discussions with several partners about bringing the the technology to the U.S. and Europe,” said founder Ekechi Nwokah. In the near-term, though, Migo is more likely to expand to Asia, he said.

 

 


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Canalys: Chinese cloud infrastructure spending reaches almost $3B a quarter

23:16 | 2 December

Canalys released its latest cloud infrastructure spending numbers for China today, and it’s all trending upward. For starters, the market reached $2.9 billion for the quarter, an increase of 60.8%. China now accounts for 10.4% of worldwide cloud spending, meaning its second only to the US in overall spending.

That is pretty amazing given that China was late in coming to the cloud, but also not surprising given the sheer size of the overall potential market. Once it got going, it was bound to gain momentum simply because of that size. Still, it is surprising that it is three times the size in terms of marketshare of the next closest country, according to Canalys.

Most of the business is going to Chinese cloud companies. Alibaba, which like Amazon has a retail arm and a cloud arm, leads the way by far with 45% of the marketshare worth $1.3 billion. Tencent is second with 18.6%, followed by AWS with 8.6% and Baidu with 8.2%. AWS was the only non-Chinese company to register any marketshare.

Wong Yih Khai, senior analyst at Canalys, says the market demand for cloud infrastructure services in China continues to grow at a rapid pace led by demand for artificial intelligence services.

“With this growing demand, cloud service providers are having to differentiate themselves in a highly competitive environment. One of the key emerging differentiators, especially among local cloud service providers, is the development of artificial intelligence (AI) capabilities, either as a service or embedded in their own offerings. AI for facial recognition is already widely used across the country in many smart city deployments and will be a key part of healthcare, retail, finance, transport and industry cloud solutions,” he said in a statement.

Interestingly enough, the marketshare breaks down somewhat like worldwide marketshare, where Amazon leads with around 34% with Microsoft in second with around 15% and Google in third with around 8%.

 


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