Sep
20
2018
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AI could help push Neo4j graph database growth

Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial intelligence and machine learning too. Today, Neo4j, the makers of the open source and commercial graph database platform, announced the release of Neo4j 3.5, which has a number of new features aimed specifically at AI and machine learning.

Neo4j founder and CEO Emil Eifrem says he had recognized the connection between AI and machine learning and graph databases for a while, but he says that it has taken some time for the market to catch up to the idea.

“There has been a lot momentum around AI and graphs…Graphs are very fundamental to AI. At the same time we were seeing some early use cases, but not really broad adoption, and that’s what we’re seeing right now,” he explained.

AI graph uses cases. Graphic: Neo4j

To help advance AI uses cases, today’s release includes a new full text search capability, which Eifrem says has been one of the most requested features. This is important because when you are making connections between entities, you have to be able to find all of the examples regardless of how it’s worded — for example, human versus humans versus people.

Part of that was building their own indexing engine to increase indexing speed, which becomes essential with ever more data to process. “Another really important piece of functionality is that we have improved our data ingestion very significantly. We have 5x end-to-end performance improvements when it comes to importing data. And this is really important for connected feature extraction, where obviously, you need a lot of data to be able to train the machine learning,” he said. That also means faster sorting of data too.

Other features in the new release include improvements to the company’s own Cypher database query language and better visualization of the graphs to give more visibility, which is useful for visualizing how machine learning algorithms work, which is known as AI explainability. They also announced support for the Go language and increased security.

Graph databases are growing increasingly important as we look to find connections between data. The most common use case is the knowledge graph, which is what lets us see connections in a huge data sets. Common examples include who we are connected to on a social network like Facebook, or if we bought one item, we might like similar items on an ecommerce site.

Other use cases include connected feature extraction, a common machine learning training techniques that can look at a lot of data and extract the connections, the context and the relationships for a particular piece of data, such as suspects in a criminal case and the people connected to them.

Neo4j has over 300 large enterprise customers including Adobe, Microsoft, Walmart, UBS and NASA. The company launched in 2007 and has raised $80 million. The last round was $36 million in November 2016.

Sep
19
2018
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IBM launches cloud tool to detect AI bias and explain automated decisions

IBM has launched a software service that scans AI systems as they work in order to detect bias and provide explanations for the automated decisions being made — a degree of transparency that may be necessary for compliance purposes not just a company’s own due diligence.

The new trust and transparency system runs on the IBM cloud and works with models built from what IBM bills as a wide variety of popular machine learning frameworks and AI-build environments — including its own Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML.

It says the service can be customized to specific organizational needs via programming to take account of the “unique decision factors of any business workflow”.

The fully automated SaaS explains decision-making and detects bias in AI models at runtime — so as decisions are being made — which means it’s capturing “potentially unfair outcomes as they occur”, as IBM puts it.

It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.

Explanations of AI decisions include showing which factors weighted the decision in one direction vs another; the confidence in the recommendation; and the factors behind that confidence.

IBM also says the software keeps records of the AI model’s accuracy, performance and fairness, along with the lineage of the AI systems — meaning they can be “easily traced and recalled for customer service, regulatory or compliance reasons”.

For one example on the compliance front, the EU’s GDPR privacy framework references automated decision making, and includes a right for people to be given detailed explanations of how algorithms work in certain scenarios — meaning businesses may need to be able to audit their AIs.

The IBM AI scanner tool provides a breakdown of automated decisions via visual dashboards — an approach it bills as reducing dependency on “specialized AI skills”.

However it is also intending its own professional services staff to work with businesses to use the new software service. So it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.

Nor is IBM the first professional services firm to spot a business opportunity around AI bias. A few months ago Accenture outed a fairness tool for identifying and fixing unfair AIs.

So with a major push towards automation across multiple industries there also looks to be a pretty sizeable scramble to set up and sell services to patch any problems that arise as a result of increasing use of AI.

And, indeed, to encourage more businesses to feel confident about jumping in and automating more. (On that front IBM cites research it conducted which found that while 82% of enterprises are considering AI deployments, 60% fear liability issues and 63% lack the in-house talent to confidently manage the technology.)

In additional to launching its own (paid for) AI auditing tool, IBM says its research division will be open sourcing an AI bias detection and mitigation toolkit — with the aim of encouraging “global collaboration around addressing bias in AI”.

“IBM led the industry in establishing trust and transparency principles for the development of new AI technologies. It’s time to translate principles into practice,” said David Kenny, SVP of cognitive solutions at IBM, commenting in a statement. “We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”

Sep
12
2018
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Nvidia launches the Tesla T4, its fastest data center inferencing platform yet

Nvidia today announced its new GPU for machine learning and inferencing in the data center. The new Tesla T4 GPUs (where the ‘T’ stands for Nvidia’s new Turing architecture) are the successors to the current batch of P4 GPUs that virtually every major cloud computing provider now offers. Google, Nvidia said, will be among the first to bring the new T4 GPUs to its Cloud Platform.

Nvidia argues that the T4s are significantly faster than the P4s. For language inferencing, for example, the T4 is 34 times faster than using a CPU and more than 3.5 times faster than the P4. Peak performance for the P4 is 260 TOPS for 4-bit integer operations and 65 TOPS for floating point operations. The T4 sits on a standard low-profile 75 watt PCI-e card.

What’s most important, though, is that Nvidia designed these chips specifically for AI inferencing. “What makes Tesla T4 such an efficient GPU for inferencing is the new Turing tensor core,” said Ian Buck, Nvidia’s VP and GM of its Tesla data center business. “[Nvidia CEO] Jensen [Huang] already talked about the Tensor core and what it can do for gaming and rendering and for AI, but for inferencing — that’s what it’s designed for.” In total, the chip features 320 Turing Tensor cores and 2,560 CUDA cores.

In addition to the new chip, Nvidia is also launching a refresh of its TensorRT software for optimizing deep learning models. This new version also includes the TensorRT inference server, a fully containerized microservice for data center inferencing that plugs seamlessly into an existing Kubernetes infrastructure.

 

 

Sep
06
2018
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PagerDuty raises $90M to wake up more engineers in the middle of the night

PagerDuty, the popular service that helps businesses monitor their tech stacks, manage incidents and alert engineers when things go sideways, today announced that it has raised a $90 million Series D round at a valuation of $1.3 billion. With this, PagerDuty, which was founded in 2009, has now raised well over $170 million.

The round was led by T. Rowe Price Associates and Wellington Management . Accel, Andreessen Horowitz and Bessemer Venture Partners participated. Given the leads in this round, chances are that PagerDuty is gearing up for an IPO.

“This capital infusion allows us to continue our investments in innovation that leverages artificial intelligence and machine learning, enabling us to help our customers transform their companies and delight their customers,” said Jennifer Tejada, CEO at PagerDuty in today’s announcement. “From a business standpoint, we can strengthen our investment in and development of our people, our most valuable asset, as we scale our operations globally. We’re well positioned to make the lives of digital workers better by elevating work to the outcomes that matter.”

Currently PagerDuty users include the likes of GE, Capital One, IBM, Spotify and virtually every other software company you’ve ever heard of. In total, more than 10,500 enterprises now use the service. While it’s best known for its alerting capabilities, PagerDuty has expanded well beyond that over the years, though it’s still a core part of its service. Earlier this year, for example, the company announced its new AIOps services that aim to help businesses reduce the amount of noisy and unnecessary alerts. I’m sure there’s a lot of engineers who are quite happy about that (and now sleep better).

Aug
29
2018
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Storage provider Cloudian raises $94M

Cloudian, a company that specializes in helping businesses store petabytes of data, today announced that it has raised a $94 million Series E funding round. Investors in this round, which is one of the largest we have seen for a storage vendor, include Digital Alpha, Fidelity Eight Roads, Goldman Sachs, INCJ, JPIC (Japan Post Investment Corporation), NTT DOCOMO Ventures and WS Investments. This round includes a $25 million investment from Digital Alpha, which was first announced earlier this year.

With this, the seven-year-old company has now raised a total of $174 million.

As the company told me, it now has about 160 employees and 240 enterprise customers. Cloudian has found its sweet spot in managing the large video archives of entertainment companies, but its customers also include healthcare companies, automobile manufacturers and Formula One teams.

What’s important to stress here is that Cloudian’s focus is on on-premise storage, not cloud storage, though it does offer support for multi-cloud data management, as well. “Data tends to be most effectively used close to where it is created and close to where it’s being used,” Cloudian VP of worldwide sales Jon Ash told me. “That’s because of latency, because of network traffic. You can almost always get better performance, better control over your data if it is being stored close to where it’s being used.” He also noted that it’s often costly and complex to move that data elsewhere, especially when you’re talking about the large amounts of information that Cloudian’s customers need to manage.

Unsurprisingly, companies that have this much data now want to use it for machine learning, too, so Cloudian is starting to get into this space, as well. As Cloudian CEO and co-founder Michael Tso also told me, companies are now aware that the data they pull in, whether from IoT sensors, cameras or medical imaging devices, will only become more valuable over time as they try to train their models. If they decide to throw the data away, they run the risk of having nothing with which to train their models.

Cloudian plans to use the new funding to expand its global sales and marketing efforts and increase its engineering team. “We have to invest in engineering and our core technology, as well,” Tso noted. “We have to innovate in new areas like AI.”

As Ash also stressed, Cloudian’s business is really data management — not just storage. “Data is coming from everywhere and it’s going everywhere,” he said. “The old-school storage platforms that were siloed just don’t work anywhere.”

Aug
21
2018
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Talla builds a smarter customer knowledge base

Talla is taking aim at the customer service industry with its latest release, an AI-infused knowledge base. Today, the company released version 2.0 of the Talla Intelligent Knowledge Base.

The company also announced that Paula Long, most recently CEO at Data Gravity, has joined the company as SVP of engineering.

This tool combines customer content with automation, chatbots and machine learning. It’s designed to help teams who work directly with customers get at the information they need faster and the machine learning element should allow it to improve over time.

You can deploy the product as a widget on your website to give customers direct access to the information, but Rob May, company founder and CEO says the most common use case involves helping sales, customer service and customer success teams get access to the most relevant and current information, whether that’s maintenance or pricing.

The information can get into the knowledge base in several ways. First of all you can enter elements like product pages and FAQs directly in the Talla product as with any knowledge base. Secondly if an employee asks a question and there isn’t an adequate answer, it exposes the gaps in information.

Talla Knowledge Base gap list. Screenshot: Talla

“It really shows you the unknown unknowns in your business. What are the questions people are asking that you didn’t realize you don’t have content for or you don’t have answers for. And so that allows you to write new content and better content,” May explained.

Finally, the company can import information into the knowledge base from Salesforce, ServiceNow, Jira or wherever it happens to live, and that can be added to a new page or incorporated into existing page as appropriate.

Employees interact with the system by asking a bot questions and it supplies the answers if one exists. It works with Slack, Microsoft Teams or Talla Chat.

Talla bot in action in Talla Chat. Screenshot: Talla

Customer service remains a major pain point for many companies. It is the direct link to customers when they are having issues. A single bad experience can taint a person’s view of a brand, and chances are when a customer is unhappy they let their friends know on social media, making an isolated incident much bigger. Having quicker access to more accurate information could help limit negative experiences.

Today’s announcement builds on an earlier version of the product that took aim at IT help desks. Talla found customers kept asking for a solution that provided similar functionality with customer-facing information and they have tuned it for that.

May launched Talla in 2015 after selling his former startup Backupify to Datto in 2014. The company, which is based near Boston, has raised $12.3 million.

Aug
17
2018
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Incentivai launches to simulate how hackers break blockchains

Cryptocurrency projects can crash and burn if developers don’t predict how humans will abuse their blockchains. Once a decentralized digital economy is released into the wild and the coins start to fly, it’s tough to implement fixes to the smart contracts that govern them. That’s why Incentivai is coming out of stealth today with its artificial intelligence simulations that test not just for security holes, but for how greedy or illogical humans can crater a blockchain community. Crypto developers can use Incentivai’s service to fix their systems before they go live.

“There are many ways to check the code of a smart contract, but there’s no way to make sure the economy you’ve created works as expected,” says Incentivai’s solo founder Piotr Grudzie?. “I came up with the idea to build a simulation with machine learning agents that behave like humans so you can look into the future and see what your system is likely to behave like.”

Incentivai will graduate from Y Combinator next week and already has a few customers. They can either pay Incentivai to audit their project and produce a report, or they can host the AI simulation tool like a software-as-a-service. The first deployments of blockchains it’s checked will go out in a few months, and the startup has released some case studies to prove its worth.

“People do theoretical work or logic to prove that under certain conditions, this is the optimal strategy for the user. But users are not rational. There’s lots of unpredictable behavior that’s difficult to model,” Grudzie? explains. Incentivai explores those illogical trading strategies so developers don’t have to tear out their hair trying to imagine them.

Protecting crypto from the human x-factor

There’s no rewind button in the blockchain world. The immutable and irreversible qualities of this decentralized technology prevent inventors from meddling with it once in use, for better or worse. If developers don’t foresee how users could make false claims and bribe others to approve them, or take other actions to screw over the system, they might not be able to thwart the attack. But given the right open-ended incentives (hence the startup’s name), AI agents will try everything they can to earn the most money, exposing the conceptual flaws in the project’s architecture.

“The strategy is the same as what DeepMind does with AlphaGo, testing different strategies,” Grudzie? explains. He developed his AI chops earning a masters at Cambridge before working on natural language processing research for Microsoft.

Here’s how Incentivai works. First a developer writes the smart contracts they want to test for a product like selling insurance on the blockchain. Incentivai tells its AI agents what to optimize for and lays out all the possible actions they could take. The agents can have different identities, like a hacker trying to grab as much money as they can, a faker filing false claims or a speculator that cares about maximizing coin price while ignoring its functionality.

Incentivai then tweaks these agents to make them more or less risk averse, or care more or less about whether they disrupt the blockchain system in its totality. The startup monitors the agents and pulls out insights about how to change the system.

For example, Incentivai might learn that uneven token distribution leads to pump and dump schemes, so the developer should more evenly divide tokens and give fewer to early users. Or it might find that an insurance product where users vote on what claims should be approved needs to increase its bond price that voters pay for verifying a false claim so that it’s not profitable for voters to take bribes from fraudsters.

Grudzie? has done some predictions about his own startup too. He thinks that if the use of decentralized apps rises, there will be a lot of startups trying to copy his approach to security services. He says there are already some doing token engineering audits, incentive design and consultancy, but he hasn’t seen anyone else with a functional simulation product that’s produced case studies. “As the industry matures, I think we’ll see more and more complex economic systems that need this.”

Aug
15
2018
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Oracle open sources Graphpipe to standardize machine learning model deployment

Oracle, a company not exactly known for having the best relationship with the open source community, is releasing a new open source tool today called Graphpipe, which is designed to simplify and standardize the deployment of machine learning models.

The tool consists of a set of libraries and tools for following the standard.

Vish Abrams, whose background includes helping develop OpenStack at NASA and later helping launch Nebula, an OpenStack startup in 2011, is leading the project. He says as his team dug into the machine learning workflow, they found a gap. While teams spend lots of energy developing a machine learning model, it’s hard to actually deploy the model for customers to use. That’s where Graphpipe comes in.

He points out that it’s common with newer technologies like machine learning for people to get caught up in the hype. Even though the development process keeps improving, he says that people often don’t think about deployment.

“Graphpipe is what’s grown out of our attempt to really improve deployment stories for machine learning models, and to create an open standard around having a way of doing that to improve the space,” Abrams told TechCrunch.

As Oracle dug into this, they identified three main problems. For starters, there is no standard way to serve APIs, leaving you to use whatever your framework provides. Next, there is no standard deployment mechanism, which leaves developers to build custom ones every time. Finally, they found existing methods leave performance as an afterthought, which in machine learning could be a major problem.

“We created Graphpipe to solve these three challenges. It provides a standard, high-performance protocol for transmitting tensor data over the network, along with simple implementations of clients and servers that make deploying and querying machine learning models from any framework a breeze,” Abrams wrote in a blog post announcing the release of Graphpipe.

The company decided to make this a standard and to open source it to try and move machine learning model deployment forward. “Graphpipe sits on that intersection between solving a business problems and pushing the state of the art forward, and I think personally, the best way to do that is by have an open source approach. Often, if you’re trying to standardize something without going for the open source bits, what you end up with is a bunch of competing technologies,” he said.

Abrams acknowledged the tension that has existed between Oracle and the open source community over the years, but says they have been working to change the perception recently with contributions to Kubernetes and Oracle FN, their open source Serverless Functions Platform as examples. Ultimately he says, if the technology is interesting enough, people will give it a chance, regardless of who is putting it out there. And of course, once it’s out there, if a community builds around it, they will adapt and change it as open source projects tend to do. Abrams hopes that happens.

“We care more about the standard becoming quite broadly adopted, than we do about our particular implementation of it because that makes it easier for everyone. It’s really up to the community decide that this is valuable and interesting.” he said.

Graphpipe is available starting today on the Oracle GitHub Graphpipe page.

Aug
13
2018
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New Uber feature uses machine learning to sort business and personal rides

Uber announced a new program today called Profile Recommendations that takes advantage of machine intelligence to reduce user error when switching between personal and business accounts.

It’s not unusual for a person to have both types of accounts. When you’re out and about, it’s easy to forget to switch between them when appropriate. Uber wants to help by recommending the correct one.

“Using machine learning, Uber can predict which profile and corresponding payment method an employee should be using, and make the appropriate recommendation,” Ronnie Gurion, GM and Global Head of Uber for Business wrote in a blog post announcing the new feature.

Uber has been analyzing a dizzying amount of trip data for so long, it can now (mostly) understand the purpose of a given trip based on the details of your request. While it’s certainly not perfect because it’s not always obvious what the purpose is, Uber believes it can determine the correct intention 80 percent of the time. For that remaining 20 percent, when it doesn’t get it right, Uber is hoping to simplify corrections too.

Photo: Uber

Business users can now also assign trip reviewers — managers or other employees who understand the employee’s usage patterns, and can flag questionable rides. Instead of starting an email thread or complicated bureaucratic process to resolve an issue, the employee can now see these flagged rides and resolve them right in the app. “This new feature not only saves the employee’s and administrator’s time, but it also cuts down on delays associated with closing out reports,” Gurion wrote in the blog post announcement.

Uber also announced that it’s supporting a slew of new expense reporting software to simplify integration with these systems. They currently have integrations with Certify, Chrome River, Concur and Expensify. They will be adding support for Expensya, Happay, Rydoo, Zeno by Serko and Zoho Expense starting in September.

All of this should help business account holders deal with Uber expenses more efficiently, while integrating with many of the leading expense programs to move data smoothly from Uber to a company’s regular record-keeping systems.

Jul
30
2018
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Google Calendar makes rescheduling meetings easier

Nobody really likes meetings — and the few people who do like them are the ones with whom you probably don’t want to have meetings. So when you’ve reached your fill and decide to reschedule some of those obligations, the usual process of trying to find a new meeting time begins. Thankfully, the Google Calendar team has heard your sighs of frustration and built a new tool that makes rescheduling meetings much easier.

Starting in two weeks, on August 13th, every guest will be able to propose a new meeting time and attach to that update a message to the organizer to explain themselves. The organizer can then review and accept or deny that new time slot. If the other guests have made their calendars public, the organizer can also see the other attendees’ availability in a new side-by-side view to find a new time.

What’s a bit odd here is that this is still mostly a manual feature. To find meeting slots to begin with, Google already employs some of its machine learning smarts to find the best times. This new feature doesn’t seem to employ the same algorithms to proposed dates and times for rescheduled meetings.

This new feature will work across G Suite domains and also with Microsoft Exchange. It’s worth noting, though, that this new option won’t be available for meetings with more than 200 attendees and all-day events.

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