The App Store saved us from carriers. But who will save us now?

When Apple introduced the App Store ten years ago, it became apparent to anyone paying attention that it was going to eat the network carriers’ lunch. Up until then, the carriers ruled the mobile ecosystem, providing the pipes, content, and services consumers were looking for. Carriers offered their own stores to download ringtones, themes, apps, and games. But Apple didn’t work with carriers (save for the iPhone exclusivity deal) because it didn’t need to. Its revolutionary iPhone and app ecosystem were clearly the future, and carriers were playing Apple’s game now.

Carriers, then, were relegated to their worst fear: becoming “dumb pipes” that simply delivered content to its customers instead of controlling it all. At first, many didn’t see what a huge paradigm shift the Apple App Store was for the mobile industry. “Apple runs its App Store in a closed environment, not sharing the revenue with AT&T. Since the average monthly phone bill for an iPhone user is $95.34 and the average bill for other users is $59.59, AT&T probably isn’t too worried about this,” wrote James Quintana Pearce for GigaOm in 2009.

Those who underestimated the power of the App Store didn’t understand that not only did users need to go to the App Store to find apps and content, but Apple managed to turn the it into a destination that consumers wanted to visit. iPhone users constantly peruse the App Store to find apps that can unlock more potential for their smartphones. The simple, universal value proposition? Whatever you want your iPhone to do, there’s an app for that.

Even as the App Store accelerated into this brave new future, the carriers remained in neutral. While networks still wanted to dictate user experience by throttling access (an approach initially defeated by net neutrality), Apple saw the wisdom of instead focusing on the end products of content and apps, rather than the pipe. Today, carriers would love to be content makers as well as content distributors: T-Mobile bundles unlimited Netflix streaming into their service, and Sprint gives Hulu away. We’ve also seen former ISPs snap up media companies, like Verizon’s purchase of Yahoo and Comcast merging with NBC Universal.

But this wasn’t yet the case in 2012. “Customers at large only want one thing: a dumb pipe,” wrote Trevor Gilbert for Pando. “The argument is that carriers have a responsibility to carry data to and fro, with no interference, just like energy and water utilities.” The “connectivity as a basic service” mantra Gilbert refers to caught on, though the idea of mobility as a public utility has yet to take root in most parts of the world.

Either way, a wave of OTT content and services, in-app transactions, P2P payments, and mobile shopping soon began to eat away at carriers’ market territory. Just three years after the App Store launched, the global telecom industry was losing 4.5% ARPU per year to apps like Skype, WhatsApp, and Facebook Messenger, much to the benefit of consumers.

Still, the networks fought back by exploring new ways to work with their rivals. Both Apple and Google Play support carrier billing as an option. “Additionally, many top social, gaming, and security segment brands have started to use carrier billing,” wrote Gerri Kodres of Fortumo in an op-ed for LightReading.com.

Today, while the networks’ share of the mobile pie is growing from carrier billing, streaming services, mobile payments, and digital wallet top-ups, they still lag behind. As a result, the elimination of net neutrality is core to their strategy to take back control and remain relevant.

Even in the midst of the just-approved Time Warner merger, which will cement the company’s footprint in the crucial realm of mobile content, AT&T is reporting consolidated revenues of $38 billion, up 13x from when the App Store came to be. The Sprint and T-Mobile merger promises them fierce competition with Verizon digging in as well. Comparatively, in the same time frame, the App Store has run from zero in 2009 to about $7 billion in 2010 to about $60 billion in 2017 (about 2x Google Play).

That said, is it really fair to still apply the “dumb” label to networks today? Have the world’s largest carriers actually been reduced to the dumb pipes consumers wanted? Perhaps not—the argument is getting tougher to make from the consumer point of view. The combination of relaxed anti-competition regulation and the death of net neutrality has huge implications for the mobile ecosystem and beyond. With networks consolidating power via acquisitions of media companies and prioritizing their own services at will, consumers may soon be faced with a mobile industry that must bow to the power of carriers.

Companies like Alphabet (Google’s parent company) and Netflix are at risk of having their services throttled in the near future. Networks have proven time and time again that if they can bully companies into paying to play, they will. Alphabet likely saw this threat coming—it has created its own “dumb pipes”: Google Fiber and Project Fi, though they are still far from becoming a threat to US carriers.

So no, carriers are no longer the “dumb pipes” consumers hoped they would be. They’ve now begun morphing into the giant monopolies of old and if they continue going unchecked, it could lead to a dearth of innovation that will affect a multitude of industries for years to come.

“One sad note though is how much the world of video is increasingly closed to startups,” writes Danny Crichton for TechCrunch. “When companies like Netflix, which today closed with a market cap of almost $158 billion, can’t necessarily get enough negotiating power to ensure that consumers have direct access to them, no startup can ever hope to compete.”

While the App Store may have wrestled away control from carriers over the last decade, we’re at a point now where the carriers are more powerful than ever. And that’s not something Apple can save us from this time.


About Katie Jansen

Katie is Chief Marketing Officer at AppLovin, a comprehensive platform where app developers of all sizes can connect with their ideal consumers and get discovered. Business Insider named Katie one of the most powerful women in mobile advertising in 2014 and 2015.

Before joining AppLovin, Katie founded the boutique marketing agency Igniting Solutions and is a board member today while it continues operation. Prior to founding Igniting Solutions, Katie was VP of Marketing at PlayFirst, a mobile gaming publisher acquired by Glu in May of 2014.

In addition to her work at AppLovin, Katie is an advocate for women in tech and workplace equality. She serves as marketing adviser to organizations including Women 2.0 and Women in Wireless, and mentors other women in tech. Katie also works with organizations such as No Kid Hungry and Restoration Missions.

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Voices in AI – Episode 58: A Conversation with Chris Eliasmith

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About this Episode

Episode 58 of Voices in AI features host Byron Reese and Chris Eliasmith talking about the brain, the mind, and emergence. Dr. Chris Eliasmith is co-CEO of Applied Brain Research, Inc. and director of the Centre for Theoretical Neuroscience at the University of Waterloo. Professor Eliasmith uses engineering, mathematics and computer modelling to study brain processes that give rise to behaviour. His lab developed the world’s largest functional brain model, Spaun, whose 2.5 million simulated neurons provide insights into the complexities of thought and action. Professor of Philosophy and Engineering, Dr. Eliasmith holds a Canada Research Chair in Theoretical Neuroscience. He has authored or coauthored two books and over 90 publications in philosophy, psychology, neuroscience, computer science, and engineering. In 2015, he won the prestigious NSERC Polayni Award. He has also co-hosted a Discovery channel television show on emerging technologies.

Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm. I’m Byron Reese. Today our guest is Chris Eliasmith. He’s the Canadian Research Chair in Theoretical Neuroscience. He’s a professor with, get this, a joint appointment in Philosophy and Systems Design Engineering and, if that’s not enough, a cross-appointment to the Computer Science department at the University of Waterloo. He is the Director of the Centre for Theoretical Neuroscience, and he was awarded the NSERC Polanyi Award for his work developing a computer model of the human brain. Welcome to the show, Chris!

Chris Eliasmith: Thank you very much. It’s great to be here.

So, what is intelligence?

That’s a tricky question, but one that I know you always like to start with. I think intelligence—I’m teaching a course on it this term, so I’ve been thinking about it a lot recently. It strikes me as the deployment of a set of skills that allow us to accomplish goals in a very wide variety of circumstances. It’s one of these things I think definitely comes in degrees, but we can think of some very stereotypical examples of the kinds of skills that seem to be important for intelligence, and these include things like abstract reasoning, planning, working with symbolic structures, and, of course, learning. I also think it’s clear that we generally don’t consider things to be intelligent unless they’re highly robust and can deal with lots of uncertainty. Basically some interesting notions of creativity often pop up when we think about what counts as intelligent or not, and it definitely depends more on how we manipulate knowledge than the knowledge we happen to have at that particular point in time.

Well, you said I like to start with that, but you were actually the first person in 56 episodes I asked that question to. I asked everybody else what artificial intelligence is, but we really have to start with intelligence. In what you just said, it sounded like there was a functional definition, like it is skills, but it’s also creativity. It’s also dealing with uncertainty. Let’s start with the most primitive thing which would be a white blood cell that can detect and kill an invading germ. Is that intelligent? I mean it’s got that skill.

I think it’s interesting that you bring that example up, because people are actually now talking about bacterial intelligence and plant intelligence. They’re definitely attempting to use the word in ways that I’m not especially comfortable with, largely because I think what you’re pointing to in these instances are sort of complex and sophisticated interactions with the world. But at the same time, I think the notions of intelligence that we’re more comfortable with are ones that deal with more cognitive kinds of behaviors, generally more abstract kinds of behaviors. The sort of degree of complexity in that kind of dealing with the world is far beyond I think what you find in things like blood cells and bacteria. Nevertheless, we can always put these things on a continuum and decide to use words in whichever particular ways we find useful. I think I’d like to restrict it to these sort of higher order kinds of complex interactions we see with…

I’m with you on that. So let me ask a different question: How is human intelligence unique in the world, as far as we know? What is different about human intelligence?

There are a couple of standard answers, I think, but even though they’re standard, I think they still capture some sort of essential insights. One of the most unique things about human intelligence is our ability to use abstract representations. We create them all the time. The most ubiquitous examples, of course, are language, where we’re just making sounds, but we can use it to refer to things in the world. We can use it to refer to classes of things in the world. We can use it to refer to things that are not in the world. We can exploit these representations to coordinate very complex social behaviors, including things like technological development as well as political systems and so on. So that sort of level of complex behavior that’s coordinated by abstract symbols is something that you just do not find in any other species on the planet. I think that’s one standard answer which I like.

The other one is that the amount of mental flexibility that humans display seems to outpace most other kinds of creatures that we see around us. This is basically just our ability to learn. One reason that people are in every single climate on the planet and able to survive in all those climates is because we can learn and adapt to unexpected circumstances. Sometimes it’s not because of abstract social reasoning or social skills or abstract language, but rather just because of our ability to develop solutions to problems which could be requiring spatial reasoning or other kinds of reasoning which aren’t necessarily guided by language.

I read, the other day, a really interesting thing, which was the only animal that will look in the direction you point is a dog, which sounds to me—I don’t know, it may be meaningless—but it sounds to me like a) we probably selected for that, right? The dog that when you say, “Go get him!” and it actually looks over there, we’d say that’s a good dog. But is there anything abstract in that, in that I point at something and then the animal then turns and looks at it?

I don’t think there’s anything especially abstract. To me, that’s an interesting kind of social coordination. It’s not the kind of abstractness I was talking about with language, I don’t think.

Okay. Do you think Gallup’s, the red dot, the thing that tries to wipe the dot off its forehead—is that a test that shows intelligence, like the creature understands what a mirror is? “Ah, that is me in the mirror?” What do you think’s going on there?

I think that is definitely an interesting test. I’m not sure how directly it’s getting at intelligence. That seems to be something more related to self-representation. Self-representation is likely something that matters for, again, social coordination, so being able to distinguish yourself from others. I think, often, more intelligent animals tend to be more social animals, likely because social interactions are so incredibly sophisticated. So you see this kind of thing definitely happening in dolphins, which are one of the animals that can pass the red dot test. You also see animals like dogs we consider generally pretty intelligent, again, because they’re very social, and that might be why they’re good at reacting to things like pointing and so on.

But it’s difficult to say that recognition in a mirror or some simple task like that is really going to let us identify something as being intelligent or not intelligent. I think the notion of intelligence is generally just much broader, and it really has to do with the set of skills—I’ll go back to my definition—the set of skills that we can bring to bear and the wide variety of circumstances that we can use on them to successfully solve problems. So when we see dolphins doing this kind of thing – they take sponges and put them on their nose so they can protect their nose from spiky animals when they’re searching the seabed, that’s an interesting kind of intelligence because they use their understanding of their environment to solve a particular problem. They also have done things like killed spiny urchins to poke eels to get them out of crevices. They’ve done all these sorts of things, it’s given the variety of problems that they’ve solved and the interesting and creative ways they’ve done it, to make us want to call dolphins intelligent. I don’t think it’s merely seeing a dot in a mirror that lets us know, “Ah! They’ve got the intelligence part of the brain.” I think it’s really a more comprehensive set of skills.

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

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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.

Voices in AI – Episode 57: A Conversation with Akshay Sabhikhi

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About this Episode

Episode 57 of Voices in AI features host Byron Reese and Akshay Sabhikhi talking about how AI augments and informs human intelligence. Akshay Sabhikhi is the CEO and Co-founder of CognitiveScale. He’s got more than 18 years of entrepreneurial leadership, product development and management experience with growth stage venture backed companies and high growth software divisions within Fortune 50 companies. He was a global leader for Smarter Care at IBM, and he successfully led and managed the acquisition of Cúram Software to establish IBM’s leadership at the intersection of social programs and healthcare. He has a BS and MS in electrical and computer engineering from UT at Austin and an MBA from the Acton School of Entrepreneurship.

Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm, I’m Byron Reese. Today my guest is Akshay Sabhikhi. He is the CEO and Co-founder of CognitiveScale. He’s got more than 18 years of entrepreneurial leadership, product development and management experience with growth stage venture backed companies and high growth software divisions within Fortune 50 companies. He was a global leader for Smarter Care at IBM, and he successfully led and managed the acquisition of Cúram Software to establish IBM’s leadership at the intersection of social programs and healthcare. He has a BS and MS in electrical and computer engineering from UT at Austin and an MBA from the Acton School of Entrepreneurship. Welcome to the show, Akshay.

Akhay Sabhikhi: Thank you Byron, great to be here.

Why is artificial intelligence working so well now? I mean like, my gosh, what has changed in the last 5-10 years?

You know, the big difference is everyone knows artificial intelligence has been around for decades, but the big difference this time as I’d like to say, is there’s a whole supporting cast of characters that’s making AI really come into its own. And it all starts firstly with the fact that it’s delivering real value to clients, so let’s dig into that.

Firstly, data is a field for AI and we all know with the amount of information we’re surrounded with, we certainly hear about big data all over the place, and you know, it’s the amount and the volume of the information, but it’s also systems that are able to interpret that information. So the type of information I’m talking about is not just your classic databases, nice neatly packaged structured information; it is highly unstructured and messy information that includes, you know, audio, video, certainly different formats of text, images, right? And our ability to really bring that data and reason over that data is a huge difference.

We talk about a second big supporting cost or supporting character here is the prominence of social, and I say social because this is the amount of data that’s available through social media, where we can in real time see consumers and how they behave, or whether it is mobile, and the fact that you have devices now in the hands of every consumer, and so you have touch points where insights can be pushed out. Those are the different, I guess supporting costs that are now there which didn’t exist before, and that’s one of the biggest changes with the prominence and true, sort of, value people are seeing with AI.

And so give us some examples, I mean you’re at the forefront of this with CognitiveScale. What are some of the things that you see that are working that wouldn’t have worked 5-10 years ago?

Well, so let’s take some examples. So, we use an analogy which is, we all sort have used WAZE as an application to get from point A to point B, right? When you look at WAZE, it’s a great consumer tool that tells you exactly what’s ahead of you: cop, traffic, debris on the road and so on, and it guides you through your journey right? Well if you look at applying a WAZE-like analogy to the enterprise where you have a patient, and I’ll use a patient as an example because that’s how we started the company. You’re largely unmanaged, all you do is you show up to your appointments, you get prescriptions, you’re told about your health condition, but then once you leave that appointment, you’re pretty much on your own right? But think about everything that’s happening around you, think about social determinants, for example, the city you live in, whether you live in the suburbs or you live in downtown, the weather patterns, the air quality, such as the pollen counts for example, or allergens that affect you or whether it is a specific zip code within the city that tells us about the food choices that exist around me.

There’s a lot of determinants that go well beyond your pure sort of structured information that comes from an electronic medical record. If you bring all of those pieces of data together, an AI system is able to look at that information and the biggest difference here being in the context of the consumer, in this case, the patient, and surface unique insights to them, but it doesn’t stop right there. What an AI system does is, it takes it a step or two further by saying, “I’m going to push insights based on what I’ve learned from data that surrounds you, and hopefully it makes sense to you. And I will give you the mechanisms to provide a thumbs up/thumbs down or specific feedback that I can then incorporate back into a system to learn from it. So that’s a real life example of an AI system that we’ve stood up for many of our clients using various kinds of structured and unstructured information to be brought together.

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

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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.

Five questions for: Mike Burrows of AgendaShift

My travels around the landscape of DevOps brought me to Mike Burrows, and the work he was doing around what he terms AgendaShift, an outcome-based approach to continuous transformation. While these words could be off-putting, I was more intrigued by the fact that Mike had set up a Slack site to articulate, test and improve his experience-based models – as he says, there’s 500 people on the site now, and as I have experienced, it’s very participative. So, what’s it all about – is there life beyond prescriptive lean and agile approaches? I sat down with Mike (in the virtual sense) to find out the background of, and hopes and dreams for, AgendaShift.

1. What led you to write a book about lean/agile/Kanban — what was being missed?

Good question! I’m one of those people that laments the rise of prescription Lean-Agile space, and though I found it easy to find people who were in sympathy with my view, I didn’t find a lot of constructive alternatives. I myself had developed a consistent approach, but calling it “non-prescriptive” only told people what it wasn’t, not what it was! Eventually, I (or perhaps I should say “we”, because I had collaborators and a growing community by this time) landed on “outcome-oriented”, and suddenly everything became a lot clearer.

2. How would you explain AgendaShift in terms a layperson might understand?

The central idea is principle #2 (of 5 – see agendashift.com/principles): Agree on outcomes. It seems kinda obvious that change will be vastly easier when you have agreement on outcomes, but most of us don’t have the tools to identify, explore, and agree on outcomes, so instead we jump to solutions, justify them, implement them over other people’s resistance, and so on. I believe that as an industry we need to move away from that 20th century model of change management, and that for Agile it is absolutely essential.

Around that central idea, we have 5 chapters modelled on the 5 sessions of our workshops, namely Discovery (establishing a sense of where we are and where we’d like to get to), Exploration (going down a level of detail, getting a better sense of the overall terrain and where the opportunities lie), Mapping (visualising it all), Elaboration (framing and developing our ideas), and Operation (treating change as real work). Everything from a corporate ambition to the potential impact of an experiment is an outcome, and we can connect the dots between them..

3. You went through an interesting development process, care to elucidate?

Two key ingredients for Agendashift are to be found in the last chapter of my first book, Kanban from the Inside (2014). The first is the idea of “keeping the agenda for change visible”, a clue to where the name “Agendashift” came from, and worthwhile to develop further how one might populate and visualise such a thing (and I took inspiration not just from Kanban, but also from Story Mapping). The second was the kind of bullet point checklist you see at the end of a lot of books.

I and a few others independently around the world (Matt Phillip most notably) realised that we had the basis for an interesting kind of assessment tool here, organised by the values of transparency, balance, collaboration and so on (the values model that was the basis for my book). In collaboration with Dragan Jojic we went through several significant iterations, broadening the assessment’s scope, removing jargon, eliminating any sense of prescription, and so on. We found that the more we did that, the more accessible it became (we now have experience using it outside of IT), and yet also more thought-provoking. Interesting!

Other collaborators – most notably Karl Scotland and Andrea Chiou – helped move Agendashift upstream into what we call Discovery, making sure than when we come to debriefing the assessment that we’re already well grounded in business context and objectives. The unexpected special ingredients there has been Clean Language (new to me at the time, and a great way to explore outcomes) and Cynefin (already very familiar to me as model, but now also very practical once we had the means to create lots of fragments of narrative, outcomes in Agendashift’s case).

4. Who is the AgendaShift book aimed at, is it appropriate for newcomers, journeymen or masters?

I do aim in my writing for “something for everyone”. I accept though that the complete newcomer to Lean-Agile or to coaching and facilitation may find that it assumes just a bit too much knowledge on the part of the reader. My third book (working title “Right to Left: The digital leader’s guide to Lean-Agile”, due 2019) will I think have the broadest possible appeal for books in this space. We’ll see!

5. How do you see things progressing – is nirvana round the corner or is that the wrong way to think about it?

We’re coming up to the 2 year anniversary of the public launch of the Agendashift partner programme, 2 years into what I’m told is likely a 3-year bootstrap process (I have some fantastic collaborators but no external investment). General interest is definitely growing – more than 500 people in the Agendashift Slack for example – and I’m seeing a significant uptick in demand for private workshops, either directly from corporates or via partner companies. Its potential as a component of leadership development and strategy deployment is gaining recognition too, so we’re not dependent only on Agile transformation opportunities. I believe that there is potential for Agendashift in the digital and DevOps spaces too.

There is a lot of vested interest in imposed Agile, and in all honesty I don’t see that changing overnight – in fact I tell people that I can see the rest of my career (I’m 53) being devoted to outcomes. Over time though, I believe that we will see more success for transformations that are based on genuine engagement, which can only be good for the likes of Agendashift, OpenSpace Agility, and so on. Eventually, the incongruity of imposed Agile will be exposed, and nirvana will be achieved 🙂

 

My take: Not the weapon, but the hand

I’m all for methodologies. Of course, I would say that – I used to run a methodology group, I trained people in better software delivery and so on. From an early stage in my career however, I learned that it is not enough to follow any set of practices verbatim: sooner or later (as I did), edge cases or a changing world will cause you to come unstuck, which goes a long way to explain why best practices seem to be in a repeated state of reinvention.

I was also lucky enough to have some fantastic mentors. Notably Barry McGibbon, who had written books about OO, and Robin Bloor, whose background was in data. Both taught me, in different ways, that all important lesson we can get from Monty Python’s Holy Grail: “It’s only a model.”

Models exist to provide a facade of simplicity, which can be an enormous boon in this complex, constantly changing age. At the same time however, they are not a thing in themselves; rather, they offer a representation. As such, it is important to understand where and when they are most suited, but also how they were created, because, quite simply, sometimes it may be quicker to create a new one than use something ill-suited for the job.

And so it is for approaches and methods, steps we work through to get a job done. Often they are right, sometimes less so. A while back, myself, Barry and others worked with Adam and Tim at DevelopmentProcess to devise a dashboard tool for developers. So many options existed, the thought of creating something generic seemed insurmountable…

… until the epiphany came, that is: while all processes require the same types of steps, their exact form, and how they were strung together, could vary. This was more than just a, “Aha! That’s how they look!” as it also put the onus onto the process creator to decide which types of step were required, in which order.

Because of this, among many other reasons, I think Mike is on to something. In another recent conversation, Tony Christensen, DevOps lead at RBS, said the goal had become to create a learning organisation, rather then transforming into some nirvanic state. True Nirvana, in this context at least, is about understanding the mechanisms available, and having the wherewithal to choose between them.

 

Image: AgendaShift

 

Voices in AI – Episode 56: A Conversation with Babak Hodjat

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About this Episode

Episode 56 of Voices in AI features host Byron Reese and Babak Hodjat talking about genetic algorithms, cyber agriculture, and sentience. Babak Hodjat is the founder and CEO of Sentient Technologies. He holds a PhD in the study of machine intelligence.

Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm, I’m Byron Reese. Today my guest is Babak Hodjat, he is the founder and CEO of Sentient Technologies. He holds a PhD in the study of machine intelligence. Welcome to the show, Babak. Rerecorded the intro

Babak Hodjat: Great to be here, thank you.

Let’s start off with my normal intro question, which is, what is artificial intelligence?

Yes, what a question. Well we know what artificial is, I think mainly the crux of this question is, “What is intelligence?”

Well actually no, there are two different senses in which it’s artificial. One is that it’s not really intelligence, it’s like artificial turf isn’t really grass, that it just looks like intelligence, but it’s not really. And the other one is, oh no it’s really intelligent it just happens to be something we made.

Yeah it’s the latter definition I think is the consensus. I’m saying this partly because there was a movement to call it machine intelligence, and there were other names to it as well, but I think artificial intelligence is, certainly the emphasis is on the fact that, as humans, we’ve been able to construct something that gives us a sense of intelligence. The main question then is, “What is this thing called intelligence?” And depending on how you answer that question, actual manifestations of AI have differed through the years.

There was a period in which AI was considered: If it tricks you into believing that it is intelligent, then it’s intelligent. So, if that’s the definition, then everything is fair game. You can cram this system with a whole bunch of rules, and back then we called them expert systems, and when you interact with these rule sets that are quite rigid, it might give you a sense of intelligence.

Then there was a movement around actually building intelligence systems, through machine learning, and mimicking how nature creates intelligence. Neural networks, genetic algorithms, some of the approaches, amongst many others that were proposed and suggested, reinforcement learning in its early form, but they would not scale. So the problem there was that they did actually show some very interesting properties of intelligence, namely learning, but they didn’t quite scale, for a number of different reasons, partly because we didn’t quite have the algorithms down yet, also the algorithms could not make use of scalable compute, and compute and memory storage was expensive.

Then we switched to redefinition in which we said, “Well, intelligence is about these smaller problem areas,” and that was the mid to late 90s where there was more interest in agenthood and agent-based systems, and agent-oriented systems where the agent was tasked with a simplified environment to solve. And intelligence was extracted into: If we were tasked with a reduced set of tools to interact with the world, and our world was much simpler than it is right now, how would we operate? That would be the definition of intelligence and those are agent based systems.

We’ve kind of swung back to machine learning based systems, partly because there have been some breakthroughs in the past, I would say 10-15 years, in neural networks in learning how to scale this technology, and an awesome rebranding of neural networks—calling them deep learning—the field has flourished on the back of that. Of course it doesn’t hurt that we have cheap compute and storage and lots and lots of data to feed these systems.

You know, one of the earlier things you said is that we try to mimic how nature creates intelligence, and you listed three examples: neural nets, and then GANNs, how we evolve things and reinforcement learning. I would probably agree with evolutionary algorithms, but do you really think… I’ve always thought neural nets, like you said, don’t really act like neurons. It’s a convenient metaphor I guess, but do you really consider neural nets to be really derived from biology or it’s just an analogy from biology?

Well it was very much inspired by biology, very much so. I mean models that we had of how we thought neurons and synapses between neurons and chemistry of the brain operates, fuels this field, absolutely. But these are very simplified versions of what the brain actually does, and every day there’s more learning about how brain cells operate. I was just reading an article yesterday about how RNA can capture memory, and how the basal ganglia also have a learning type of function—it’s not just the pre-frontal cortex. There’s a lot of complexity and depth in how the brain operates, that is completely lost when you simplify it. So absolutely we’re inspired definitely, but this is not a model of the brain by any stretch of the imagination.

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

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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.

Voices in AI – Episode 55: A Conversation with Rob High

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About this Episode

Episode 55 of Voices in AI features host Byron Reese and Rob High talking about IBM Watson and the history and future of AI. Rob High is an IBM fellow, VP and Chief Technical Officer at IBM Watson.

Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

Transcript Excerpt

Byron Reese: This is Voices in AI, brought to you by GigaOm. I’m Byron Reese. August 12th, 1981. That was the day IBM released the IBM PC and who could have imagined what that would lead to? Who would’ve ever thought, from that vantage point, of our world today? Who could’ve imagined that eventually you would have one on every desktop and then they would all be connected? Who would have guessed that through those connections, trillions of dollars of wealth would be created? All the companies, you know, that you see in the news every day from eBay to Amazon to Google to Baidu to Alibaba, all of them have, in one way or the other, as the seed of their genesis, that moment on August 12th, 1981.

Now the interesting thing about that date, August of ‘81, that’s kind of getting ready to begin the school year, the end of the summer. And it so happens that our guest, Rob High graduated from UC Santa Cruz in 1981, so he graduated about the same time, just a few months before this PC device was released. And he went and joined up with IBM. And for the last 36/37 years, he has been involved in that organization affecting what they’re doing, watching it all happen, and if you think about it, what a journey that must be. If you ever pay your respects to Elvis Presley and see his tombstone, you’ll see it says, “He became a living legend in his own time.” Now, I’ll be the first to say that’s a little redundant, right? He was either a living legend or a legend in his own time. That being said, if there’s anybody who can be said to be a living legend in his own time, it’s our guest today. It’s Rob High. He is an IBM fellow, he is a VP at IBM, he is the Chief Technical Officer at IBM Watson and he is with us today. Welcome to the show, Rob!

Rob High: Yeah, thank you very much. I appreciate the references but somehow I think my kids would consider those accolades to be a little, probably, you know, not accurate.

Well, but from a factual standpoint, you joined IBM in 1981 when the PC was brand new.

Yeah – I’ve really been honored with having the opportunity to work on some really interesting problems over the years. And with that honor has come the responsibility to bring value to those problems, to the solutions we have for those problems. And for that, I’ve always been well-recognized. So I do appreciate you bringing that up. In fact, it really is more than just any one person in this world that makes changes meaningful.

Well, so walk me back to that. Don’t worry, this isn’t going to be a stroll down memory lane, but I’m curious. In 1981, IBM was of course immense, as immense as it is now and the PC had to be a kind of tiny part of that at that moment in time. It was new. When did your personal trajectory intercept with that or did it ever? Had you always been on the bigger system side of IBM?

No, actually. It was almost immediate. Probably was, I don’t know the exact number, but probably I was pretty close to the first one hundred or two hundred people that ordered a PC when it got announced. In fact, the first thing I did at IBM was to take the PC into work and show my colleagues what the potential was. I was just doing simple, silly things at the time, but I wanted to make an impression that this really was going to change the way that we were thinking about our roles at work and what technology was going to do to help change our trajectory there. So, no, I actually had the privilege of being there at the very beginning. I won’t say that I had the foresight to recognize its utility but I certainly appreciated it and I think that to some extent, my own career followed the trajectory of change that has occurred similar to what PCs did to us back then. In other areas as well: including web computing, and service orientation, now cloud computing, and of course cognitive computing.

And so, walk me through that and then let’s jump into Watson. So, walk me through the path you went through as this whole drama of the computer age unfolded around you. Where did you go from point to point to point through that and end up where you are now?

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

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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.

Voices in AI – Episode 54: A Conversation with Ahmad Abdulkader

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About this Episode

Episode 54 of Voices in AI features host Byron Reese and Ahmad Abdulkader talking about the brain, learning, and education as well as privacy and AI policy. Ahmad Abdulkader is the CTO of Voicera. Before that he was the technical lead of Facebook’s DeepText, an AI text understanding engine. Prior to that he developed OCR engines, machine learning systems, and computer vision systems at Google.

Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm. I am Byron Reese. Today our guest is Ahmad Abdulkader. He is the CTO of Voicera. Before that he was the lead architect for Facebook supplied AI efforts producing Deep Texts, which is a text understanding engine. Prior to that he worked at Google building OCR engines, machine learning systems, and computer vision systems. He holds a Bachelor of Science and Electrical Engineering degree from Cairo University and a Masters in Computer Science from the University of Washington. Welcome to the show.

Ahmad Abdulkader: Thank you, thanks Byron, thanks for having me.

I always like to start out by just asking people to define artificial intelligence because I have never had two people define it the same way before.

Yeah, I can imagine. I am not aware of a formal definition. So, to me AI is the ability of machines to do or perform cognitive tasks that humans can do or learn to do rather. And eventually learn to do it in a seamless way.

Is the calculator therefore artificial intelligence?

No, the calculator is not performing a cognitive task. A cognitive task I mean vision, speech understanding, understanding text, and such. Actually, in fact the brain is actually lousy at multiplying two six-digit numbers, which is what the calculator is good at. But the calculator is really bad at doing a cognitive test.

I see, well actually, that is a really interesting definition because you’re defining it not by some kind of an abstract notion of what it means to be intelligent, but you’ve got a really kind of narrow set of skills that once something can do those, it’s an AI. Do I understand you correctly?

Right, right, I have a sort of a yard stick, or I have a sort of a set of tasks a human can do in a seamless easy way without even knowing how to do it, and we want to actually have machines mimic that to some degree. And there will be some very specific set of tasks, some of them are more important than others and so far, we haven’t been able to build machines that actually get even close to the human beings around these tasks.

Help me understand how you are seeing the world that way, and I don’t want to get caught up on definitions, but this is really interesting.

Right.

So, if a computer couldn’t read, couldn’t recognize objects, and couldn’t do all those things you just said, but let’s say it was creative and it could write novels. Is that an AI?

First of all, this is hypothetical. I wouldn’t know, I wouldn’t call it AI, so it goes back to the definition of intelligence, and then there’s a natural intelligence that humans exhibit, and then there is artificial intelligence that machines will attempt to make and exhibit. So, the most important of these that we actually use sort of almost every second of the day are vision, speech understanding, or language understanding, and creativity is one of them. So if you were to do that I would say this machine performed a subset of AI, but haven’t exhibited the behavior to show that’s it good at the most important ones, being vision, speech and such.

When you say vision and speech are the most important ones, nobody’s ever really looked at the problem this way, so I really want to understand how you’re saying that, because it would seem to me those aren’t really the most important by a long shot. I mean, if I had an AI that could diagnose any disease, tell us how to generate unlimited energy, fix all the environmental woes, tell us how to do faster than light travel, all of those things, like, feed the hungry, and alleviate poverty and all of those things, but they couldn’t tell a tuna fish from a Land Rover. I would say that’s pretty important, I would take that hands down over what you’re calling to be more important stuff.

I think really important is an overloaded word. I think you’re talking about utility, right? So, you’re imagining a hypothetical situation where we’re able to build computers that will do the diagnosis or poverty and stuff like that. These would be way more useful for us, or that’s what we think, or that’s the hypothesis. But actually to do these tasks that you’re talking about, it probably implies, most probably that you have done or solved, to a great degree, solved vision. It’s hard to imagine that you would be doing diagnosis without actually solving vision. So, these are sort of the basic tasks that actually humans can do, and babies learn, and we see babies or children learn this as they grow up. So, perhaps the utility of what you talked about would be much more useful for us, but if you were to define importance as sort of the basic skills that you could build upon, I would say vision would be the most important one. Language understanding perhaps would be the second most important one. And I think doing well in these basic cognitive skills would enable us to solve the problems that you’re talking about.

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

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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.