The Ins and Outs of Knit Theory

Source: https://www.nytimes.com/2019/05/17/science/math-physics-knitting-matsumoto.html?partner=rss&emc=rss
May 17, 2019 at 12:00PM

For physicist Elisabetta Matsumoto, “knitting is coding,” and yarn is a programmable material.

Violent Japanese Mascot That Tickled John Oliver Is Suspended by Twitter

Source: https://www.nytimes.com/2019/05/17/world/asia/chiitan-mascot-john-oliver-japan.html?partner=rss&emc=rss
May 17, 2019 at 07:33AM

The mascot Chiitan, modeled on a real-life otter, drew notoriety for violent stunts that delighted social media users and the HBO comedian.

SpaceX Again Delays Starlink Internet Satellite Launch

Source: https://www.nytimes.com/2019/05/16/science/spacex-launch.html?partner=rss&emc=rss
May 17, 2019 at 04:09AM

Elon Musk’s company will wait until next week to test its system that aims to provide high-speed internet all over the world.

Pinterest Posts Narrower Loss, but Falls Short of Wall St. Estimates

Source: https://www.nytimes.com/2019/05/16/technology/pinterest-earnings.html?partner=rss&emc=rss
May 17, 2019 at 01:24AM

The digital pinboard company’s stock price tumbled in after-hours trading after its first earnings report as a public company.

Unstructured Data Management, Where to Start

Source: https://gigaom.com/2019/05/16/unstructured-data-management-where-to-start/
May 16, 2019 at 06:29PM

A few months ago I wrote a report about unstructured data management. And the introduction starts like this: “Unstructured data growth is hardly news anymore. In fact, the challenge is no longer exponential growth, which we are now accustomed to and have solutions for, but it is all about keeping data safe while giving access to users, applications, and devices distributed globally, as well as having control over it.”

Data storage has dramatically evolved in the last few years, and there are several aspects to take into account before planning for a data management strategy:

• Unstoppable Data Growth – The reality is that we cannot stop nor contain data growth, especially when it comes to unstructured data. It is not only that the sources that can produce data are growing by the day with the increasing amount of devices at our disposal, sensors cameras, and so on. It is also about the fact that documents are richer, bigger and we keep them for a longer period of time. Regulations and never-delete policies don’t help either. $/GB of our storage infrastructures, both in terms of TCA and TCO are key to infrastructure and business sustainability.
• Unpracticable Consolidation – We create data everywhere but, still today, for many organizations consolidating it in a single place is pure utopia. Sometimes this is about organizational issues, in other cases, it is more about connectivity constraints. No matter the case, the result is the same, file servers and NAS devices are installed everywhere or, at least, access gateways need to be close to the applications and users needing that data. As a result, infrastructure becomes more complex over time, it needs to be protected, secured and managed accordingly.
• Edge-Core-Cloud – Core and Edge data storage are now joined by the cloud. This is an additional challenge, as well as an opportunity. In fact, it could add complexity to the picture, but if leveraged in the right way it can quickly become an opportunity for large savings as well as introduce enhanced data protection mechanisms and data management tools.

There are several ways to plan a data management strategy and tools to execute this as well. Some of them are more intrusive, while others have a minimal impact on the existing infrastructure. At the end of the day, data management all depends on the results you want to get, how quickly, and the resources at your disposal to start this type of initiative.

Do you want to know more?

On May 21 (12 pm CDT), I’ll be hosting a webinar with the title “Unstructured Data Management: A New Way of Fighting Data,” I’ll be joined by Krishna Subramanian (COO of Komprise) and we will discuss unstructured data management in all its aspects. The session will be interactive with polls and a Q&A session at the end of the presentation. We will discuss business and technical aspects of data management including, what it is, how to start, its benefits, the kind of savings you can get from it, and how it can impact the way you do business. Join us by registering here, the webinar is free but seating is limited.

Voices in AI – Episode 87: A Conversation with Sameer Maskey

Source: https://gigaom.com/2019/05/16/voices-in-ai-episode-87-a-conversation-with-sameer-maskey/
May 16, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

Episode 87 of Voices in AI features Byron speaking with Sameer Maskey of Fusemachines about the development of machine learning, languages and AI capabilities.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm and I’m Byron Reese. Today my guest is Sameer Maskey. He is the founder and CEO of Fusemachines and he’s an adjunct assistant professor at Columbia. He holds an undergraduate degree in Math and Physics from Bates College and a PhD in Computer Science from Columbia University as well. Welcome to the show, Sameer.

Sameer Maskey: Thanks Byron, glad to be here.

Can you recall the first time you ever heard the term ‘artificial intelligence’ or has it always just been kind of a fixture of your life?

It’s always been a fixture of my life. But the first time I heard about it in the way it is understood in today’s world of what AI is, was in my first year undergrad when I was thinking of building talking machines. That was my dream, building a machine that can sort of converse with you. And in doing that research I happened to run into several books on AI and particularly a book called Voice and Speech Synthesis, and that’s how my journey in AI came into fruition.

So a conversational AI, it sounds like something that I mean I assume early on you heard about the Turing Test and thought ‘I wonder how you would build a device that could pass that.’ Is that fair to say?

Yeah, I’d heard about Turing test but my interest stemmed from being able to build a machine that could just talk, read a book and then talk with you about it. And I was particularly interested on being able to build the machine in Nepal. So I grew up in Nepal and I was always interested in building machines that can talk Nepali. So more than the Turing Test was just this notion of ‘can we build a machine that can talk in Nepali and converse with you?’

Would that require a general intelligence or are not anywhere near a general intelligence? For it to be able to like read a book and then have a conversation with you about The Great Gatsby or whatever. Would that require general intelligence?

Being able to build a machine that can read a book and then just talk about it would require I guess what is being termed as artificial general intelligence. That begs many different other kinds of question of what AGI is and how it’s different from AI in what form. But we are still quite far ways from being able to build a machine that can just read a novel or a history book and then just be able to sit down with you and discuss it. I think we are quite far away from it even though there’s a lot of research being done from a conversational AI perspective.

Yeah I mean the minute a computer can learn something, you can just point it at the Internet and say “go learn everything” right?

Exactly. And we’re not there, at all.

Pedro Domingo wrote a book called The Master Algorithm. He said he believes there is like some uber algorithm yet we haven’t discovered which accounts for intelligence in all of its variants, and part of the reason he believes that is, we’re made with shockingly little code DNA. And the amount of that code which is different than a chimp, say, you may only be six or seven mbps in that tiny bit of code. It doesn’t have intelligence obviously, but it knows how to build intelligence. So is it possible that… do you think that that level of artificial intelligence, whether you want to call it AGI or not but that level of AI, do you think that might be a really simple thing that we just haven’t… that’s like right in front of us and we can’t see it? Or do you think it’s going to be a long hard slog to finally get there and it’ll be a piece at a time?

To answer that question and to sort of be able to say maybe there is this Master Algorithm that’s just not discovered, I think it’s hard to make anything towards it, because we as a human being even neurologically and neuroscientists and so forth don’t even fully understand how all the pieces of the cognition work. Like how my four and a half year old kid is just able to learn from couple of different words and put together and start having conversations about it. So I think we don’t even understand how human brains work. I get a little nervous when people claim or suggest there’s this one master algorithm that’s just yet to be discovered.

We had this one trick that is working now where we take a bunch of data about the past and we study it with computers and we look for patterns, and we use those patterns to predict the future. And that’s kind of what we do. I mean that’s machine learning in a nutshell. And it’s hard for me for instance to see how will that ever write The Great Gatsby, let alone read it and understand it, but how could it ever be creative? But maybe it can be.

Through one lens, we’re not that far with AI and why do you think it’s turning out to be so hard? I guess that’s my question. Why is AI so hard? We’re intelligent and we can kind of reflect on our own intelligence and we kind of figure out how we learn things, but we have this brute force way of just cramming a bunch of data down the machine’s throat, and then it can spot spam email or route you through traffic and nothing else. So why is AI turning out to be so hard?

Because I think the machinery that’s been built over many, many years on how AI has evolved and is to a point right now, like you pointed out it is still a lot of systems looking at a lot of historical data, building models that figure out patterns on it and doing predictions on it and it requires a lot of data. And one of the reasons deep learning is working very well is there’s so much data right now.

We haven’t figured out how, with a very little bit of data you can create generalization on the patterns to be able to do things. And that piece on how to model or build a machine that can generalize decision making process based on just a few pieces of information… we haven’t figured that out. And until we figure that out, it is still going to be very hard to make AGI or a system that can just write The Great Gatsby. And I don’t know how long will it be until we figure that part out.

A lot of times people think that a general intelligence is just an evolutionary product from narrow. We get narrow then we get…First they can play Go and then it can play all games, all strategy games. And then it can do this and it gets better and better and then one day it’s general.

Is it possible that what we know how to do now has absolutely nothing to do with general intelligence? Like we haven’t even started working on that problem, it’s a completely different problem. All we’re able to do is make things that can fake intelligence, but we don’t know how to make anything that’s really intelligent. Or do you think we are on a path that’s going to just get better and better and better until one day we have something that can make coffee and play Go and compose sonnets?

There is some new research being done on AGI, but the path right now which is where we train more and more data on bigger and bigger architecture and sort of simulate our fake intelligence, I don’t think that would probably lead into solutions that can have general intelligence the way we are talking about. It is still a very similar model that we’ve been using before, and that’s been invented a long time ago.

They are much more popular right now because they can do more with more data with more compute power and so forth. So when it is able to drive a car based on computer vision and neural net and learning behind it, it simulates intelligence. But it’s not really probably the way we describe human intelligence, so that it can write books and write poetry. So are we on the path to AGI? I don’t think that with the current evolution of the way the machinery is done is probably going to lead you to AGI. There’s probably some fundamental new ways of exploring things that is required and how the problem is framed to sort of thinking about how general intelligence works.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

[voices_in_ai_link_back]

Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

Hardware as a Service – Resellers Becoming Managed Service Providers

Source: https://gigaom.com/2019/05/15/hardware-as-a-service-resellers-becoming-managed-service-providers/
May 15, 2019 at 04:33PM

In an effort to mimic the cloud, vendors, as well as resellers, are switching to subscription-based sales models for hardware and, according to the end users I spoke to, it sounds like it’s working out very well.

Last week Infinidat announced its – all-inclusive – pay-as-you-go subscription option for their Infinibox. They are not the first to do something like this. The Pure Storage Evergreen program was probably the first to hit the market a while back and has been praised for its characteristics and flexibility, but it’s definitely a good thing for Infinidat’s customers.

These kinds of programs are truly all-inclusive. In fact, they not only include hardware and maintenance but also unlimited system upgrades, migration services and so on. At the end of the day, it simplifies the procurement process, improves financial aspects of HW infrastructure management by reducing CAPEX (capital expenditure) in favor of OPEX (operating expenses), and provisioning is simplified as well.

Now that we live in an era of hybrid cloud infrastructures, this kind of approach helps organizations of all sizes to limit the friction between on-prem and cloud as well as traditional and next-gen IT environments.

Resellers are Becoming MSPs

What struck me the most about this idea of Hardware as a Service (HaaS) is that resellers in the field, here in Europe at least, are embracing this idea and making it theirs. More and more resellers are transforming their business and becoming MSPs (managed service providers), packaging their solutions in a cloud-like fashion and selling them as subscriptions.

To get a better understanding of this phenomenon, there are two questions that need to be addressed: Why? and How?

Why?

It’s actually quite simple. End users are finally understanding the cloud and they like the idea. Even the most conservative end users are experimenting with it. At the same time, small and medium enterprises love their relationship with local partners, however, the risk of resellers losing customers to the cloud is becoming higher by the day. Resellers are becoming more aware of this situation and are reacting accordingly.

How?

Many resellers are starting to propose solutions which come very close to what you can get from the vendors, taking advantage of their position and the flexibility they can get from it. You can think of them as cloud and hardware brokers. While most do not own a data center, they can access colocation facilities, cloud providers and whatever is necessary to build personalized solutions for their customers. They then are able to sell the entire package with the added value that comes from integration and the necessary services to make it work. The end user only needs to manage its applications and data, offloading most of the infrastructure issues to their partner.

It’s also very interesting to note that these kinds of solutions are very cost-competitive as well. In fact, resellers (or MSPs if you like) are very careful to purchase at the best price possible to be more competitive and improve their margin. Furthermore, this new subscription-based business model allows them to have more bargaining power with the vendors. In the end, they become the end user from the vendor’s perspective, so the final customer gets a solution and a partner, rather than just a bunch of products. Thus by changing their business model, resellers have access to a different economy of scale for their purchases. For example, a few days ago a reseller told me that he bought 1PB licenses of a SDS (software-defined-storage) solution and his team is now slicing it in 20, 30, and 100TB chunks for its customers. The vendor’s sales representative was happy to close a single 1PB deal, and now the reseller will be able to provide a very good $/GB to small customers while making higher profits.

And it’s not only about HW and SW, sometimes it’s also about the cloud. These reseller-MSPs can buy from large providers like Amazon, Google or Microsoft, but they can also choose to work with other providers like Wasabi, Backblaze or Packet, just to name a few, that can give them raw resources with better prices, more control over the stack, less lock-in and, white labeled solutions. Once again, it’s more about leveraging internal skills and the relationship with the end user to build something that is much more tailored, hybrid, perfectly aligned with users’ needs and very competitive from the economic and financial perspective.

Closing the Circle

The channel is evolving pretty quickly to adapt itself to the cloud era. Here in Europe at least, many resellers are switching to innovative business models to keep up with the times. The feedback I’ve received from the field in the last few months is very positive and end users love the idea of getting a cloud-like experience through their beloved partners.

Trump’s Latest Move Takes Straight Shot at Huawei’s Business

Source: https://www.nytimes.com/2019/05/16/technology/huawei-ban-president-trump.html?partner=rss&emc=rss
May 16, 2019 at 06:05PM

By restricting the Chinese technology giant’s ability to buy American components, Washington could hamstring Huawei’s operations worldwide.

Voices in AI – Episode 87: A Conversation with Sameer Maskey

Source: https://gigaom.com/2019/05/16/voices-in-ai-episode-87-a-conversation-with-sameer-maskey/
May 16, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

Episode 87 of Voices in AI features Byron speaking with Sameer Maskey of Fusemachines about the development of machine learning, languages and AI capabilities.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm and I’m Byron Reese. Today my guest is Sameer Maskey. He is the founder and CEO of Fusemachines and he’s an adjunct assistant professor at Columbia. He holds an undergraduate degree in Math and Physics from Bates College and a PhD in Computer Science from Columbia University as well. Welcome to the show, Sameer.

Sameer Maskey: Thanks Byron, glad to be here.

Can you recall the first time you ever heard the term ‘artificial intelligence’ or has it always just been kind of a fixture of your life?

It’s always been a fixture of my life. But the first time I heard about it in the way it is understood in today’s world of what AI is, was in my first year undergrad when I was thinking of building talking machines. That was my dream, building a machine that can sort of converse with you. And in doing that research I happened to run into several books on AI and particularly a book called Voice and Speech Synthesis, and that’s how my journey in AI came into fruition.

So a conversational AI, it sounds like something that I mean I assume early on you heard about the Turing Test and thought ‘I wonder how you would build a device that could pass that.’ Is that fair to say?

Yeah, I’d heard about Turing test but my interest stemmed from being able to build a machine that could just talk, read a book and then talk with you about it. And I was particularly interested on being able to build the machine in Nepal. So I grew up in Nepal and I was always interested in building machines that can talk Nepali. So more than the Turing Test was just this notion of ‘can we build a machine that can talk in Nepali and converse with you?’

Would that require a general intelligence or are not anywhere near a general intelligence? For it to be able to like read a book and then have a conversation with you about The Great Gatsby or whatever. Would that require general intelligence?

Being able to build a machine that can read a book and then just talk about it would require I guess what is being termed as artificial general intelligence. That begs many different other kinds of question of what AGI is and how it’s different from AI in what form. But we are still quite far ways from being able to build a machine that can just read a novel or a history book and then just be able to sit down with you and discuss it. I think we are quite far away from it even though there’s a lot of research being done from a conversational AI perspective.

Yeah I mean the minute a computer can learn something, you can just point it at the Internet and say “go learn everything” right?

Exactly. And we’re not there, at all.

Pedro Domingo wrote a book called The Master Algorithm. He said he believes there is like some uber algorithm yet we haven’t discovered which accounts for intelligence in all of its variants, and part of the reason he believes that is, we’re made with shockingly little code DNA. And the amount of that code which is different than a chimp, say, you may only be six or seven mbps in that tiny bit of code. It doesn’t have intelligence obviously, but it knows how to build intelligence. So is it possible that… do you think that that level of artificial intelligence, whether you want to call it AGI or not but that level of AI, do you think that might be a really simple thing that we just haven’t… that’s like right in front of us and we can’t see it? Or do you think it’s going to be a long hard slog to finally get there and it’ll be a piece at a time?

To answer that question and to sort of be able to say maybe there is this Master Algorithm that’s just not discovered, I think it’s hard to make anything towards it, because we as a human being even neurologically and neuroscientists and so forth don’t even fully understand how all the pieces of the cognition work. Like how my four and a half year old kid is just able to learn from couple of different words and put together and start having conversations about it. So I think we don’t even understand how human brains work. I get a little nervous when people claim or suggest there’s this one master algorithm that’s just yet to be discovered.

We had this one trick that is working now where we take a bunch of data about the past and we study it with computers and we look for patterns, and we use those patterns to predict the future. And that’s kind of what we do. I mean that’s machine learning in a nutshell. And it’s hard for me for instance to see how will that ever write The Great Gatsby, let alone read it and understand it, but how could it ever be creative? But maybe it can be.

Through one lens, we’re not that far with AI and why do you think it’s turning out to be so hard? I guess that’s my question. Why is AI so hard? We’re intelligent and we can kind of reflect on our own intelligence and we kind of figure out how we learn things, but we have this brute force way of just cramming a bunch of data down the machine’s throat, and then it can spot spam email or route you through traffic and nothing else. So why is AI turning out to be so hard?

Because I think the machinery that’s been built over many, many years on how AI has evolved and is to a point right now, like you pointed out it is still a lot of systems looking at a lot of historical data, building models that figure out patterns on it and doing predictions on it and it requires a lot of data. And one of the reasons deep learning is working very well is there’s so much data right now.

We haven’t figured out how, with a very little bit of data you can create generalization on the patterns to be able to do things. And that piece on how to model or build a machine that can generalize decision making process based on just a few pieces of information… we haven’t figured that out. And until we figure that out, it is still going to be very hard to make AGI or a system that can just write The Great Gatsby. And I don’t know how long will it be until we figure that part out.

A lot of times people think that a general intelligence is just an evolutionary product from narrow. We get narrow then we get…First they can play Go and then it can play all games, all strategy games. And then it can do this and it gets better and better and then one day it’s general.

Is it possible that what we know how to do now has absolutely nothing to do with general intelligence? Like we haven’t even started working on that problem, it’s a completely different problem. All we’re able to do is make things that can fake intelligence, but we don’t know how to make anything that’s really intelligent. Or do you think we are on a path that’s going to just get better and better and better until one day we have something that can make coffee and play Go and compose sonnets?

There is some new research being done on AGI, but the path right now which is where we train more and more data on bigger and bigger architecture and sort of simulate our fake intelligence, I don’t think that would probably lead into solutions that can have general intelligence the way we are talking about. It is still a very similar model that we’ve been using before, and that’s been invented a long time ago.

They are much more popular right now because they can do more with more data with more compute power and so forth. So when it is able to drive a car based on computer vision and neural net and learning behind it, it simulates intelligence. But it’s not really probably the way we describe human intelligence, so that it can write books and write poetry. So are we on the path to AGI? I don’t think that with the current evolution of the way the machinery is done is probably going to lead you to AGI. There’s probably some fundamental new ways of exploring things that is required and how the problem is framed to sort of thinking about how general intelligence works.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

[voices_in_ai_link_back]

Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

SpaceX Delays Launch of Starlink Orbital Internet Satellites

Source: https://www.nytimes.com/2019/05/15/science/spacex-launch.html?partner=rss&emc=rss
May 16, 2019 at 05:59AM

The mission is to be a test of the company’s ambitions to create a new line of business by connecting more parts of the world.