New York vs. Grubhub

Source: https://www.nytimes.com/2019/09/30/business/grubhub-seamless-restaurants-delivery-apps-fees.html?emc=rss&partner=rss
September 30, 2019 at 05:34PM

Restaurant owners say that Grubhub’s business model has cut into their profit margins. A New York City Council committee is investigating.

China Plays ‘Fight the Landlord’ to Tame Hong Kong

Source: https://www.nytimes.com/2019/09/30/business/china-hong-kong-li-ka-shing-business.html?emc=rss&partner=rss
September 30, 2019 at 12:00PM

State media puts pressure on Li Ka-shing, a powerful property tycoon, showing the Communist Party’s view of business as a means of control.

Voices in AI – Bonus: A Conversation with Hilary Mason

Source: https://gigaom.com/2019/09/23/voices-in-ai-bonus-a-conversation-with-hilary-mason/
September 23, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this 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 am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence.

Yeah, did you know ancient people, not even that far back, like Roman times, had vastly better memories than we had? We know of one Roman general that knew the names of all 25,000 of his troops and the names of all their families. Yet, Plato wasn’t a big fan of writing for that very reason. He said that with writing, you’ve invented a system for reminding yourself but not for remembering anything. He predicted that once literacy was widespread, our memories would go to pot, and he was right. Like you, I can’t remember my PIN# half the time!

That’s incredible!

I guess my real question, though, is when you ask people – “well, when will we have a general intelligence?” you have a range of answers. You have five years for—Elon Musk used that timeline and then to 500. Andrew Ng is worrying about such things as overpopulation on Mars. The reason the range is so high is nobody knows how to build a general intelligence. Would you agree with that?

Yes, I would agree, and I would firmly state that I do not believe there is a technical path from where we are today to that form of general intelligence.

You know that’s a fantastic observation because machine learning, our trick du jour, is an idea that says: ‘let’s take information about the past, study it, look for patterns, and project them into the future.’ That may not be a path to general intelligence. Is that what you’re saying?

That is what I’m saying. That we know how to build systems that look at data and make predictions or forecasts that infer things that we can’t even directly observe, which is remarkable. We do not know how to make systems that mimic intelligence in ways that would distinguish it from the systems or from humans.

I’ve had 100 guests on this show – and they virtually all believe we could/can, with your caveat about the timeframe, create a general intelligence, even though they all agree we don’t know how to do it. The reason those two things are compatible is they have a simple assumption that is: humans are machines, specifically our brains are machines. You know how the thought experiment goes… if you could take what a neuron did and model that and then did that a hundred billion times and figured out what the glial cells do and all that other stuff, there’s no reason you can’t build a general intelligence.

Do you believe people are machines, or our brains are purely mechanistic in the sense that there’s nothing about them that cannot be described with physics, really?

So I do believe that, with the caveat that we don’t necessarily understand all of that physics, necessarily today. I do think there is a biological and physical basis for human intelligence, and that should we understand it well enough, we could possibly construct something that’s indistinguishable. But we certainly don’t understand it and we may need to invent entire new fields of physics before we would.

Listen to this 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.

Voices in AI – Bonus: A Conversation with Hilary Mason

Source: https://gigaom.com/2019/09/23/voices-in-ai-bonus-a-conversation-with-hilary-mason/
September 23, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this 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 am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence.

Yeah, did you know ancient people, not even that far back, like Roman times, had vastly better memories than we had? We know of one Roman general that knew the names of all 25,000 of his troops and the names of all their families. Yet, Plato wasn’t a big fan of writing for that very reason. He said that with writing, you’ve invented a system for reminding yourself but not for remembering anything. He predicted that once literacy was widespread, our memories would go to pot, and he was right. Like you, I can’t remember my PIN# half the time!

That’s incredible!

I guess my real question, though, is when you ask people – “well, when will we have a general intelligence?” you have a range of answers. You have five years for—Elon Musk used that timeline and then to 500. Andrew Ng is worrying about such things as overpopulation on Mars. The reason the range is so high is nobody knows how to build a general intelligence. Would you agree with that?

Yes, I would agree, and I would firmly state that I do not believe there is a technical path from where we are today to that form of general intelligence.

You know that’s a fantastic observation because machine learning, our trick du jour, is an idea that says: ‘let’s take information about the past, study it, look for patterns, and project them into the future.’ That may not be a path to general intelligence. Is that what you’re saying?

That is what I’m saying. That we know how to build systems that look at data and make predictions or forecasts that infer things that we can’t even directly observe, which is remarkable. We do not know how to make systems that mimic intelligence in ways that would distinguish it from the systems or from humans.

I’ve had 100 guests on this show – and they virtually all believe we could/can, with your caveat about the timeframe, create a general intelligence, even though they all agree we don’t know how to do it. The reason those two things are compatible is they have a simple assumption that is: humans are machines, specifically our brains are machines. You know how the thought experiment goes… if you could take what a neuron did and model that and then did that a hundred billion times and figured out what the glial cells do and all that other stuff, there’s no reason you can’t build a general intelligence.

Do you believe people are machines, or our brains are purely mechanistic in the sense that there’s nothing about them that cannot be described with physics, really?

So I do believe that, with the caveat that we don’t necessarily understand all of that physics, necessarily today. I do think there is a biological and physical basis for human intelligence, and that should we understand it well enough, we could possibly construct something that’s indistinguishable. But we certainly don’t understand it and we may need to invent entire new fields of physics before we would.

Listen to this 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 Unveils Silvery Vision to Mars: ‘It’s Basically an I.C.B.M. That Lands’

Source: https://www.nytimes.com/2019/09/29/science/elon-musk-spacex-starship.html?emc=rss&partner=rss
September 29, 2019 at 03:42PM

Elon Musk delivered an update on his company’s Starship prototype, which faces business challenges and neighbors not happy to live so close to its test site.

Ahead of 2020, Facebook Falls Short on Plan to Share Data on Disinformation

Source: https://www.nytimes.com/2019/09/29/technology/facebook-disinformation.html?emc=rss&partner=rss
September 29, 2019 at 03:00PM

The social network says it has struggled to get the information to researchers because it also wants to protect its users’ privacy.

An Explosion in Online Child Sex Abuse: What You Need to Know

Source: https://www.nytimes.com/2019/09/29/us/takeaways-child-sex-abuse.html?emc=rss&partner=rss
September 29, 2019 at 07:36AM

Emerging tech platforms and overwhelmed law enforcement agencies have contributed to a boom in digital abuse imagery, a Times investigation found. Here are some key takeaways.

Preying on Children: The Emerging Psychology of Pedophiles

Source: https://www.nytimes.com/2019/09/29/us/pedophiles-online-sex-abuse.html?emc=rss&partner=rss
September 29, 2019 at 07:19AM

Images of child sex abuse have reached a crisis point on the internet. Now, science is beginning to shed light on why people abuse children in the first place.

Voices in AI – Bonus: A Conversation with Hilary Mason

Source: https://gigaom.com/2019/09/23/voices-in-ai-bonus-a-conversation-with-hilary-mason/
September 23, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this 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 am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence.

Yeah, did you know ancient people, not even that far back, like Roman times, had vastly better memories than we had? We know of one Roman general that knew the names of all 25,000 of his troops and the names of all their families. Yet, Plato wasn’t a big fan of writing for that very reason. He said that with writing, you’ve invented a system for reminding yourself but not for remembering anything. He predicted that once literacy was widespread, our memories would go to pot, and he was right. Like you, I can’t remember my PIN# half the time!

That’s incredible!

I guess my real question, though, is when you ask people – “well, when will we have a general intelligence?” you have a range of answers. You have five years for—Elon Musk used that timeline and then to 500. Andrew Ng is worrying about such things as overpopulation on Mars. The reason the range is so high is nobody knows how to build a general intelligence. Would you agree with that?

Yes, I would agree, and I would firmly state that I do not believe there is a technical path from where we are today to that form of general intelligence.

You know that’s a fantastic observation because machine learning, our trick du jour, is an idea that says: ‘let’s take information about the past, study it, look for patterns, and project them into the future.’ That may not be a path to general intelligence. Is that what you’re saying?

That is what I’m saying. That we know how to build systems that look at data and make predictions or forecasts that infer things that we can’t even directly observe, which is remarkable. We do not know how to make systems that mimic intelligence in ways that would distinguish it from the systems or from humans.

I’ve had 100 guests on this show – and they virtually all believe we could/can, with your caveat about the timeframe, create a general intelligence, even though they all agree we don’t know how to do it. The reason those two things are compatible is they have a simple assumption that is: humans are machines, specifically our brains are machines. You know how the thought experiment goes… if you could take what a neuron did and model that and then did that a hundred billion times and figured out what the glial cells do and all that other stuff, there’s no reason you can’t build a general intelligence.

Do you believe people are machines, or our brains are purely mechanistic in the sense that there’s nothing about them that cannot be described with physics, really?

So I do believe that, with the caveat that we don’t necessarily understand all of that physics, necessarily today. I do think there is a biological and physical basis for human intelligence, and that should we understand it well enough, we could possibly construct something that’s indistinguishable. But we certainly don’t understand it and we may need to invent entire new fields of physics before we would.

Listen to this 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.

Elon Musk to Deliver SpaceX Starship Presentation

Source: https://www.nytimes.com/2019/09/28/science/elon-musk-spacex-starship.html?emc=rss&partner=rss
September 28, 2019 at 08:14PM

The founder of the private launch company will offer a major update on its next major rocket.

The Week the C.E.O.s Got Smacked

Source: https://www.nytimes.com/2019/09/28/business/wework-juul-ebay-ceo.html?emc=rss&partner=rss
September 28, 2019 at 12:00PM

Their message was inspired. Their returns, not so much.

Voices in AI – Bonus: A Conversation with Hilary Mason

Source: https://gigaom.com/2019/09/23/voices-in-ai-bonus-a-conversation-with-hilary-mason/
September 23, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this 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 am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence.

Yeah, did you know ancient people, not even that far back, like Roman times, had vastly better memories than we had? We know of one Roman general that knew the names of all 25,000 of his troops and the names of all their families. Yet, Plato wasn’t a big fan of writing for that very reason. He said that with writing, you’ve invented a system for reminding yourself but not for remembering anything. He predicted that once literacy was widespread, our memories would go to pot, and he was right. Like you, I can’t remember my PIN# half the time!

That’s incredible!

I guess my real question, though, is when you ask people – “well, when will we have a general intelligence?” you have a range of answers. You have five years for—Elon Musk used that timeline and then to 500. Andrew Ng is worrying about such things as overpopulation on Mars. The reason the range is so high is nobody knows how to build a general intelligence. Would you agree with that?

Yes, I would agree, and I would firmly state that I do not believe there is a technical path from where we are today to that form of general intelligence.

You know that’s a fantastic observation because machine learning, our trick du jour, is an idea that says: ‘let’s take information about the past, study it, look for patterns, and project them into the future.’ That may not be a path to general intelligence. Is that what you’re saying?

That is what I’m saying. That we know how to build systems that look at data and make predictions or forecasts that infer things that we can’t even directly observe, which is remarkable. We do not know how to make systems that mimic intelligence in ways that would distinguish it from the systems or from humans.

I’ve had 100 guests on this show – and they virtually all believe we could/can, with your caveat about the timeframe, create a general intelligence, even though they all agree we don’t know how to do it. The reason those two things are compatible is they have a simple assumption that is: humans are machines, specifically our brains are machines. You know how the thought experiment goes… if you could take what a neuron did and model that and then did that a hundred billion times and figured out what the glial cells do and all that other stuff, there’s no reason you can’t build a general intelligence.

Do you believe people are machines, or our brains are purely mechanistic in the sense that there’s nothing about them that cannot be described with physics, really?

So I do believe that, with the caveat that we don’t necessarily understand all of that physics, necessarily today. I do think there is a biological and physical basis for human intelligence, and that should we understand it well enough, we could possibly construct something that’s indistinguishable. But we certainly don’t understand it and we may need to invent entire new fields of physics before we would.

Listen to this 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.

Twitch Users Watch Billions of Hours of Video, but the Site Wants to Go Beyond Fortnite

Source: https://www.nytimes.com/2019/09/26/business/media/twitch-twitchcon-ads-redesign.html?emc=rss&partner=rss
September 26, 2019 at 10:27PM

The live-streaming platform, owned by Amazon, has more viewers than many cable channels. But a new ad campaign aims to show it’s good for more than video games.

The Week in Tech: Why Californians Have Better Privacy Protections

Source: https://www.nytimes.com/2019/09/27/technology/the-week-in-tech-why-californians-have-better-privacy-protections.html?emc=rss&partner=rss
September 27, 2019 at 04:00PM

While Congress has stalled on new privacy bills, a real estate developer is pushing for broader data rights in the Golden State.

Voices in AI – Bonus: A Conversation with Hilary Mason

Source: https://gigaom.com/2019/09/23/voices-in-ai-bonus-a-conversation-with-hilary-mason/
September 23, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this 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 am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence.

Yeah, did you know ancient people, not even that far back, like Roman times, had vastly better memories than we had? We know of one Roman general that knew the names of all 25,000 of his troops and the names of all their families. Yet, Plato wasn’t a big fan of writing for that very reason. He said that with writing, you’ve invented a system for reminding yourself but not for remembering anything. He predicted that once literacy was widespread, our memories would go to pot, and he was right. Like you, I can’t remember my PIN# half the time!

That’s incredible!

I guess my real question, though, is when you ask people – “well, when will we have a general intelligence?” you have a range of answers. You have five years for—Elon Musk used that timeline and then to 500. Andrew Ng is worrying about such things as overpopulation on Mars. The reason the range is so high is nobody knows how to build a general intelligence. Would you agree with that?

Yes, I would agree, and I would firmly state that I do not believe there is a technical path from where we are today to that form of general intelligence.

You know that’s a fantastic observation because machine learning, our trick du jour, is an idea that says: ‘let’s take information about the past, study it, look for patterns, and project them into the future.’ That may not be a path to general intelligence. Is that what you’re saying?

That is what I’m saying. That we know how to build systems that look at data and make predictions or forecasts that infer things that we can’t even directly observe, which is remarkable. We do not know how to make systems that mimic intelligence in ways that would distinguish it from the systems or from humans.

I’ve had 100 guests on this show – and they virtually all believe we could/can, with your caveat about the timeframe, create a general intelligence, even though they all agree we don’t know how to do it. The reason those two things are compatible is they have a simple assumption that is: humans are machines, specifically our brains are machines. You know how the thought experiment goes… if you could take what a neuron did and model that and then did that a hundred billion times and figured out what the glial cells do and all that other stuff, there’s no reason you can’t build a general intelligence.

Do you believe people are machines, or our brains are purely mechanistic in the sense that there’s nothing about them that cannot be described with physics, really?

So I do believe that, with the caveat that we don’t necessarily understand all of that physics, necessarily today. I do think there is a biological and physical basis for human intelligence, and that should we understand it well enough, we could possibly construct something that’s indistinguishable. But we certainly don’t understand it and we may need to invent entire new fields of physics before we would.

Listen to this 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.

Voices in AI – Bonus: A Conversation with Hilary Mason

Source: https://gigaom.com/2019/09/23/voices-in-ai-bonus-a-conversation-with-hilary-mason/
September 23, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this 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 am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence.

Yeah, did you know ancient people, not even that far back, like Roman times, had vastly better memories than we had? We know of one Roman general that knew the names of all 25,000 of his troops and the names of all their families. Yet, Plato wasn’t a big fan of writing for that very reason. He said that with writing, you’ve invented a system for reminding yourself but not for remembering anything. He predicted that once literacy was widespread, our memories would go to pot, and he was right. Like you, I can’t remember my PIN# half the time!

That’s incredible!

I guess my real question, though, is when you ask people – “well, when will we have a general intelligence?” you have a range of answers. You have five years for—Elon Musk used that timeline and then to 500. Andrew Ng is worrying about such things as overpopulation on Mars. The reason the range is so high is nobody knows how to build a general intelligence. Would you agree with that?

Yes, I would agree, and I would firmly state that I do not believe there is a technical path from where we are today to that form of general intelligence.

You know that’s a fantastic observation because machine learning, our trick du jour, is an idea that says: ‘let’s take information about the past, study it, look for patterns, and project them into the future.’ That may not be a path to general intelligence. Is that what you’re saying?

That is what I’m saying. That we know how to build systems that look at data and make predictions or forecasts that infer things that we can’t even directly observe, which is remarkable. We do not know how to make systems that mimic intelligence in ways that would distinguish it from the systems or from humans.

I’ve had 100 guests on this show – and they virtually all believe we could/can, with your caveat about the timeframe, create a general intelligence, even though they all agree we don’t know how to do it. The reason those two things are compatible is they have a simple assumption that is: humans are machines, specifically our brains are machines. You know how the thought experiment goes… if you could take what a neuron did and model that and then did that a hundred billion times and figured out what the glial cells do and all that other stuff, there’s no reason you can’t build a general intelligence.

Do you believe people are machines, or our brains are purely mechanistic in the sense that there’s nothing about them that cannot be described with physics, really?

So I do believe that, with the caveat that we don’t necessarily understand all of that physics, necessarily today. I do think there is a biological and physical basis for human intelligence, and that should we understand it well enough, we could possibly construct something that’s indistinguishable. But we certainly don’t understand it and we may need to invent entire new fields of physics before we would.

Listen to this 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.

Wall Street Skeptics Poke at Start-Up Bubble

Source: https://www.nytimes.com/2019/09/26/business/tech-ipo-market.html?emc=rss&partner=rss
September 27, 2019 at 02:28AM

Companies like WeWork and Uber were expected to become a new generation of corporate giants. But investors have backed away.

Facebook Tests Hiding ‘Likes’ on Social Media Posts

Source: https://www.nytimes.com/2019/09/26/technology/facebook-hidden-likes.html?emc=rss&partner=rss
September 27, 2019 at 01:15AM

The social network, which has been under fire for extreme content on its site, said it was testing making Likes to posts private in Australia.

Group Behind California Privacy Law Aims to Strengthen It

Source: https://www.nytimes.com/2019/09/24/technology/group-behind-california-privacy-law-aims-to-strengthen-it.html?emc=rss&partner=rss
September 25, 2019 at 03:06AM

The leaders of Californians for Consumer Privacy say they want to amend the law, which goes into effect next year, through a ballot initiative.

Sleep Therapy for the Masses May Be Coming to You Soon

Source: https://www.nytimes.com/2019/09/24/technology/cvs-health-insomnia-app.html?emc=rss&partner=rss
September 24, 2019 at 05:23PM

CVS Health is encouraging employers to provide a sleep app that could help push digital therapeutics into the mainstream.

‘Nerd,’ ‘Nonsmoker,’ ‘Wrongdoer’: How Might A.I. Label You?

Source: https://www.nytimes.com/2019/09/20/arts/design/imagenet-trevor-paglen-ai-facial-recognition.html?emc=rss&partner=rss
September 24, 2019 at 07:18AM

ImageNet Roulette, a digital art project and viral selfie app, exposes how biases have crept into the artificial-intelligence technologies changing our lives.

One Brother Stabbed the Other. The Journalist Who Wrote About It Paid a Price.

Source: https://www.nytimes.com/2019/09/23/technology/right-to-be-forgotten-law-europe.html?emc=rss&partner=rss
September 24, 2019 at 05:16AM

The use of Europe’s “right to be forgotten” privacy law has broadened, illustrated by two Italian brothers, a stabbing and the journalist who wrote about them.

As HBO Celebrates a Big Night, Questions About Its Future Loom

Source: https://www.nytimes.com/2019/09/23/business/media/hbo-emmys-streaming-competition-amazon-netflix.html?emc=rss&partner=rss
September 24, 2019 at 03:04AM

The cable network won more Emmys than anyone else on Sunday night, but competition is growing and its new leadership has little entertainment experience.

Apple Keeps Making Computer in Texas After Tariff Waivers

Source: https://www.nytimes.com/2019/09/23/technology/apple-mac-pro-texas-tariffs.html?emc=rss&partner=rss
September 24, 2019 at 01:28AM

Apple’s announcement ended a monthslong public dance with the White House over tariffs and the company’s ability to build products in the United States.

China Scores Businesses, and Low Grades Could Be a Trade-War Weapon

Source: https://www.nytimes.com/2019/09/22/business/china-social-credit-business.html?emc=rss&partner=rss
September 23, 2019 at 10:18PM

Beijing hopes its social credit system will quickly punish companies accused of wrongdoing. U.S. firms could get hit, too.

M.I.T. Media Lab, Already Rattled by the Epstein Scandal, Has a New Worry

Source: https://www.nytimes.com/2019/09/22/business/media/mit-media-lab-food-computer.html?emc=rss&partner=rss
September 23, 2019 at 11:23AM

Former researchers for a “food computer” initiative at the lab say the project’s leader misled outsiders about how it was going.

The Week in Tech: An Emerging Twist on Antitrust

Source: https://www.nytimes.com/2019/09/20/technology/big-tech-antitrust.html?emc=rss&partner=rss
September 23, 2019 at 07:21AM

If regulators and lawmakers are serious, they’re going to have to rethink a traditional approach to monopolies.

Twitter Suspends Account of Former Adviser to Saudi Crown Prince

Source: https://www.nytimes.com/2019/09/20/technology/twitter-suspension-middle-east.html?emc=rss&partner=rss
September 21, 2019 at 08:58PM

The account was one of thousands with ties to governments in the Middle East that were taken down by the social media company.

Congress Asks More than 80 Companies for Big Tech Complaints

Source: https://www.nytimes.com/2019/09/20/technology/house-antitrust-investigation-big-tech.html?emc=rss&partner=rss
September 21, 2019 at 02:56AM

House lawmakers asked the companies for information on how their businesses had been affected by Amazon, Apple, Facebook and Google.

Seven Ways Telecommuting Has Changed Real Estate

Source: https://www.nytimes.com/2019/09/20/realestate/how-telecommuting-has-changed-real-estate.html?emc=rss&partner=rss
September 21, 2019 at 02:07AM

As more people are able to work from home, housing priorities have changed, and different places and types of housing have become more popular.

Inside Airbnb, Employees Eager for Big Payouts Pushed It to Go Public

Source: https://www.nytimes.com/2019/09/20/technology/airbnb-employees-ipo-payouts.html?emc=rss&partner=rss
September 21, 2019 at 02:02AM

Tension has grown among a 6,000-person work force as it waits to sell company shares, people with knowledge of the situation said.

Facebook’s Suspension of ‘Tens of Thousands’ of Apps Reveals Wider Privacy Issues

Source: https://www.nytimes.com/2019/09/20/technology/facebook-data-privacy-suspension.html?emc=rss&partner=rss
September 21, 2019 at 01:34AM

The scale of suspensions, following the Cambridge Analytica scandal, was far larger than the social network had previously revealed.

Secret F.B.I. Subpoenas Scoop Up Personal Data From Scores of Companies

Source: https://www.nytimes.com/2019/09/20/us/data-privacy-fbi.html?emc=rss&partner=rss
September 20, 2019 at 10:47PM

The practice, which the bureau says is vital to counterterrorism efforts, casts a much wider net than previously disclosed, newly released documents show.

Funny or Die Finds New Life in the Streaming Era

Source: https://www.nytimes.com/2019/09/20/business/media/funny-or-die-streaming.html?emc=rss&partner=rss
September 20, 2019 at 09:11PM

The company, which began as a comedy website, has branched out to podcasts and feature films, including “Between Two Ferns: The Movie,” which debuts on Netflix on Friday.

Uber and Lyft Drivers Gain Labor Clout, With Help From an App

Source: https://www.nytimes.com/2019/09/20/business/uber-lyft-drivers.html?emc=rss&partner=rss
September 20, 2019 at 12:00PM

A nascent group in California offers a model for organizing a far-flung work force, and wielding political influence, through innovative technology.

Voices in AI – Bonus: A Conversation with Hilary Mason

Source: https://gigaom.com/2019/09/23/voices-in-ai-bonus-a-conversation-with-hilary-mason/
September 23, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this 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 am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence.

Yeah, did you know ancient people, not even that far back, like Roman times, had vastly better memories than we had? We know of one Roman general that knew the names of all 25,000 of his troops and the names of all their families. Yet, Plato wasn’t a big fan of writing for that very reason. He said that with writing, you’ve invented a system for reminding yourself but not for remembering anything. He predicted that once literacy was widespread, our memories would go to pot, and he was right. Like you, I can’t remember my PIN# half the time!

That’s incredible!

I guess my real question, though, is when you ask people – “well, when will we have a general intelligence?” you have a range of answers. You have five years for—Elon Musk used that timeline and then to 500. Andrew Ng is worrying about such things as overpopulation on Mars. The reason the range is so high is nobody knows how to build a general intelligence. Would you agree with that?

Yes, I would agree, and I would firmly state that I do not believe there is a technical path from where we are today to that form of general intelligence.

You know that’s a fantastic observation because machine learning, our trick du jour, is an idea that says: ‘let’s take information about the past, study it, look for patterns, and project them into the future.’ That may not be a path to general intelligence. Is that what you’re saying?

That is what I’m saying. That we know how to build systems that look at data and make predictions or forecasts that infer things that we can’t even directly observe, which is remarkable. We do not know how to make systems that mimic intelligence in ways that would distinguish it from the systems or from humans.

I’ve had 100 guests on this show – and they virtually all believe we could/can, with your caveat about the timeframe, create a general intelligence, even though they all agree we don’t know how to do it. The reason those two things are compatible is they have a simple assumption that is: humans are machines, specifically our brains are machines. You know how the thought experiment goes… if you could take what a neuron did and model that and then did that a hundred billion times and figured out what the glial cells do and all that other stuff, there’s no reason you can’t build a general intelligence.

Do you believe people are machines, or our brains are purely mechanistic in the sense that there’s nothing about them that cannot be described with physics, really?

So I do believe that, with the caveat that we don’t necessarily understand all of that physics, necessarily today. I do think there is a biological and physical basis for human intelligence, and that should we understand it well enough, we could possibly construct something that’s indistinguishable. But we certainly don’t understand it and we may need to invent entire new fields of physics before we would.

Listen to this 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.

A.I. Researchers See Danger of Haves and Have-Nots

Source: https://www.nytimes.com/2019/09/26/technology/ai-computer-expense.html?emc=rss&partner=rss
September 26, 2019 at 10:00AM

A.I. research is becoming increasingly expensive, leaving few people with easy access to the computing firepower necessary to develop the technology.

At Least 70 Countries Have Engaged in Disinformation Campaigns, Study Finds

Source: https://www.nytimes.com/2019/09/26/technology/government-disinformation-cyber-troops.html?emc=rss&partner=rss
September 26, 2019 at 07:01AM

Governments are using “cyber troops” to discredit political opponents, bury opposing views and interfere in foreign affairs, according to Oxford researchers.

Voices in AI – Bonus: A Conversation with Hilary Mason

Source: https://gigaom.com/2019/09/23/voices-in-ai-bonus-a-conversation-with-hilary-mason/
September 23, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this 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 am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence.

Yeah, did you know ancient people, not even that far back, like Roman times, had vastly better memories than we had? We know of one Roman general that knew the names of all 25,000 of his troops and the names of all their families. Yet, Plato wasn’t a big fan of writing for that very reason. He said that with writing, you’ve invented a system for reminding yourself but not for remembering anything. He predicted that once literacy was widespread, our memories would go to pot, and he was right. Like you, I can’t remember my PIN# half the time!

That’s incredible!

I guess my real question, though, is when you ask people – “well, when will we have a general intelligence?” you have a range of answers. You have five years for—Elon Musk used that timeline and then to 500. Andrew Ng is worrying about such things as overpopulation on Mars. The reason the range is so high is nobody knows how to build a general intelligence. Would you agree with that?

Yes, I would agree, and I would firmly state that I do not believe there is a technical path from where we are today to that form of general intelligence.

You know that’s a fantastic observation because machine learning, our trick du jour, is an idea that says: ‘let’s take information about the past, study it, look for patterns, and project them into the future.’ That may not be a path to general intelligence. Is that what you’re saying?

That is what I’m saying. That we know how to build systems that look at data and make predictions or forecasts that infer things that we can’t even directly observe, which is remarkable. We do not know how to make systems that mimic intelligence in ways that would distinguish it from the systems or from humans.

I’ve had 100 guests on this show – and they virtually all believe we could/can, with your caveat about the timeframe, create a general intelligence, even though they all agree we don’t know how to do it. The reason those two things are compatible is they have a simple assumption that is: humans are machines, specifically our brains are machines. You know how the thought experiment goes… if you could take what a neuron did and model that and then did that a hundred billion times and figured out what the glial cells do and all that other stuff, there’s no reason you can’t build a general intelligence.

Do you believe people are machines, or our brains are purely mechanistic in the sense that there’s nothing about them that cannot be described with physics, really?

So I do believe that, with the caveat that we don’t necessarily understand all of that physics, necessarily today. I do think there is a biological and physical basis for human intelligence, and that should we understand it well enough, we could possibly construct something that’s indistinguishable. But we certainly don’t understand it and we may need to invent entire new fields of physics before we would.

Listen to this 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.

Voices in AI – Episode 96: A Conversation with Gary Marcus

Source: https://gigaom.com/2019/09/19/voices-in-ai-episode-96-a-conversation-with-gary-marcus/
September 19, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

Episode 96 of Voices in AI features Byron speaking with author and psychologist Gary Marcus about the nature of intelligence and what the mind really means in relation to AI.

Listen to this 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 Bryon Reese. Today our guest is Gary Marcus. He is a scientist, author, and entrepreneur. He’s a professor in the Department of Psychology at NYU. He was the founder and CEO of Geometric Intelligence, a machine learning company later acquired by Uber. He has a new company called Robust.AI and a new book called Rebooting AI, so we should have a great chat. Welcome to the show, Gary.

Gary Marcus: Thanks very much for having me.

Why is intelligence such a hard thing to define, and why is artificial intelligence artificial? Is it really intelligence, or is it just something that can mimic intelligence, or is there not a difference between those two things?

I think different people have different views about that. I’m not doctrinaire about vocabulary. I think that intelligence itself is a multidimensional variable. People want to stuff it into a single number and say your IQ is 110, or 160, or 92, or whatever it is, but there are really many things that go into natural intelligence such as the ability to solve problems you haven’t seen before, or the ability to recognize objects, or the ability to speak or to be very verbal about it. There’s many, many different dimensions to intelligence. When we talk about artificial intelligence, we’re basically talking about whether machines can do some of those things.

You’re a provocative guy with all kinds of ideas in all different areas. Talk a little bit about the mind, how you think it comes about in 30 seconds or less, please. And will artificial intelligence need to have a mind to do a lot of the things we want it to do?

The best thing I ever heard about that, short version, is Steven Pinker was on Stephen Colbert. Colbert asked him to explain the brain in five words, and he said brain cells firing patterns. That’s how our brains work is there’s a lot of neural firing, and minds emerge from the activity of those brains. We still don’t really understand what all that means. We don’t have a very good grip on what the neural processes are that give rise to basic things like speaking sentences. We have a long way to go understanding it in those terms.

I tend to take a psychologist’s perspective more than a neuroscience perspective and say the mind is all of our cognitive functions. It’s how we think and how we reason, how we understand our place in the world. Machines, if we want to get to the point where they’re trustworthy, are going to have to do many of the things that human minds do, not necessarily in identical ways. It has to be able to capture, for example, the flexibility that human minds have, such that when they encounter something they haven’t seen before, they can cope with it and not just break down.

I know you said you don’t usually approach it from neurology, but I’m fascinated by the nematode worm who’s got just a handful of neurons. People have spent so long, 20 years in the OpenWorm project, trying to model those 302 neurons to make behavior. They’re not even sure it’s even possible to do that.

Do you think we are going to have to crack that code and understand something about how the brain works before we can build truly intelligent machines, or is it like the old saw about airplanes and birds [flying differently]? They’re going to think in a way that’s alien to the way we think?

I think it’s somewhere in between, but I’m also pushing towards the psychology side. I don’t think that understanding the connectome of the human brain or all those connections is anytime soon going to really help us with AI. I do think that understanding psychology better, like how people reason about everyday objects as they navigate the world, that might actually help us.

Psychology isn’t as much of a prestige discipline, so to speak, as neuroscience. Neuroscience gets more money, gets more attention. Neuroscience will probably tell us a lot about the nature of intelligence in the long term. That could be a long term of 50 or 100 years. Meanwhile, thinking about psychology has actually led to some AI that I think really works. None of it’s what we call artificial general intelligence. Most of the AI we have doesn’t owe that much to neuroscience, and if anything, it owes something to psychology and people trying to figure out how human beings or other animals solve problems.

Yeah, I agree completely with that. I think AI tries to glom onto things like neural nets and all of that to try to give them some biological tie, but I think it’s more marketing than anything.

I was about to say exactly that. I think it’s more marketing than anything.Neural networks are very, very, loosely modeled on the brain. I’m trying to think of a metaphor. It’d be like comparing a child’s first drawing to some incredibly elaborate work of art. Okay, they’re both drawings, but they’re really not the same thing. Neural networks, for example, only have essentially one kind of neuron, which either fires or doesn’t. Biology, first of all, separates the firing neurons from the inhibiting neurons, the positive from the negatives, and then there are probably 1,000 different kinds of neurons in the brain with many different properties. The so-called neural networks that people are using don’t have any of that. We don’t really understand how the biology works, so people just ignore it. They wind up with something that is only superficially related to how that brain actually functions.

Let’s talk about consciousness. Consciousness is the experience of being you, obviously. A computer can measure temperature, but we can feel warmth. I’ve heard it described as the last great scientific question we know neither how to pose scientifically nor what the answer would look like. Do you think that’s a fair description of the problem of consciousness?

The only part I’m going to give you grief about is that it’s the last great scientific question. I mean, as you yourself said later in your question, it’s not a well-formed question. Great scientific questions are well formed. We know what an answer would look like and what a methodology would be for answering them. Maybe we lack some instrument. We can’t do it yet. We need a bigger collider or something like that where we understand the principle of how you can get data to address it. [With] consciousness, we don’t really at this point know that.

We don’t know even what a ‘consciousness meter’ would look like. If we had one, we’d go around and do a bunch of experiments and say, “Well, does this worm that you’re talking about have consciousness? Does my cat? What if I’m asleep? What if I’m in a coma?” You could start to collect data. You could build a theory around that. We don’t even know how we would collect the data.

My view is: there is something there that needs to be answered. Obviously, there is a feeling of experiencing red, or experiencing orgasm, or whatever we would describe as consciousness. We don’t have any, I think, real scientific purchase on what it is that we’re even asking. Maybe it will turn out to be the last great scientific question, but if it is, it’ll be somehow refined relative to what it is that we’re asking right now.

Do you believe that we can create a general intelligence on some time period measured in centuries, even? Do you believe it’s possible to do that?

I do, absolutely. I’m widely known as a critic of AI, but I’m only a critic of what people are doing now, which I think is misguided in certain ways. I certainly think it’s possible to build a general intelligence. You could argue on the margins. Could a machine be conscious? I would say, “Well, it depends what you mean by conscious, and I don’t know what the answer is.”

Could you build a machine that could be a much more flexible thinker than current machines? Yes, I don’t see a principled reason why you couldn’t have a machine that was as smart as MacGyver and could figure out how to get its way out of a locked room using twist ties and rubber bands or something like that, which a current machine can’t do at all. I don’t see the principled reason why computers can’t do that, and I see at least some notion of how we might move more in that direction.

The problem right now is: people are very attracted to using large databases. We’re in the era of big data, and almost all of the research is around what you can do with big data. That leads to solutions to certain kinds of problems. How do I recognize a picture and label it if I have a lot of labels from other people that have taken similar pictures? It doesn’t necessarily lead you to questions about what would I do if I had this small amount of data, and I was addressing a problem that nobody had ever seen before? That’s what humans are good at, and that’s what’s lacking from machines. This doesn’t mean it’s an unsolvable problem in principle. It means that people are chasing research dollars and salary and stuff like that for a certain set of problems that are popular right now. My view is that AI is misguided right now, but not that it’s impossible.

Listen to this 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.

Amazon Wants Alexa to Move (With You) Far Beyond the Living Room

Source: https://www.nytimes.com/2019/09/25/technology/amazon-alexa-new-devices.html?emc=rss&partner=rss
September 26, 2019 at 01:09AM

New devices were introduced on Wednesday to get the digital assistant in your earbuds, in your eyeglasses and in your bathroom.

How to Make the Most of Apple’s New Privacy Tools in iOS 13

Source: https://www.nytimes.com/2019/09/25/technology/personaltech/privacy-tools-apple-ios-13.html?emc=rss&partner=rss
September 25, 2019 at 05:13PM

We tested the new suite of privacy tools in Apple’s latest mobile software, from minimizing location sharing to silencing robocalls.

Improvising a Laptop Recorder and Chewing Gum at the Same Time

Source: https://www.nytimes.com/2019/09/25/technology/personaltech/disinformation-slack.html?emc=rss&partner=rss
September 25, 2019 at 04:00PM

Davey Alba, a Times newcomer who reports on disinformation, has a taste for Ice Breakers and a trick for saving audio from her computer.

EBay C.E.O. Steps Down

Source: https://www.nytimes.com/2019/09/25/technology/ebay-ceo-steps-down.html?emc=rss&partner=rss
September 25, 2019 at 04:38PM

EBay said Chief Executive Officer Devin Wenig has stepped down and the e-commerce company named its finance head Scott Schenkel as interim C.E.O.

There’s a New iPod Touch. Yes, in 2019, and Yes, It’s Worth Looking at.

Source: https://www.nytimes.com/2019/09/18/smarter-living/ipod-touch-review.html?emc=rss&partner=rss
September 23, 2019 at 07:22AM

Sure, there’s a new iPhone, but Apple’s launch of a new iPod Touch earlier this year came with laughter from some. Others see an opportunity.

Plastic Surgery and the Secret World of Instagram Dolls

Source: https://www.nytimes.com/2019/09/25/style/instagram-plastic-surgery-doll-accounts.html?emc=rss&partner=rss
September 25, 2019 at 12:00PM

An Instagram community of “doll pages” lets women find valuable information about body-sculpting journeys.

The Family Minivan as Reporting Tool

Source: https://www.nytimes.com/2019/09/18/technology/personaltech/minivan-reporting-tool.html?emc=rss&partner=rss
September 19, 2019 at 07:03AM

The finance editor, David Enrich, does a lot of work while on the move (but don’t worry, he’s not behind the wheel).

Voices in AI – Bonus: A Conversation with Hilary Mason

Source: https://gigaom.com/2019/09/23/voices-in-ai-bonus-a-conversation-with-hilary-mason/
September 23, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this 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 am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence.

Yeah, did you know ancient people, not even that far back, like Roman times, had vastly better memories than we had? We know of one Roman general that knew the names of all 25,000 of his troops and the names of all their families. Yet, Plato wasn’t a big fan of writing for that very reason. He said that with writing, you’ve invented a system for reminding yourself but not for remembering anything. He predicted that once literacy was widespread, our memories would go to pot, and he was right. Like you, I can’t remember my PIN# half the time!

That’s incredible!

I guess my real question, though, is when you ask people – “well, when will we have a general intelligence?” you have a range of answers. You have five years for—Elon Musk used that timeline and then to 500. Andrew Ng is worrying about such things as overpopulation on Mars. The reason the range is so high is nobody knows how to build a general intelligence. Would you agree with that?

Yes, I would agree, and I would firmly state that I do not believe there is a technical path from where we are today to that form of general intelligence.

You know that’s a fantastic observation because machine learning, our trick du jour, is an idea that says: ‘let’s take information about the past, study it, look for patterns, and project them into the future.’ That may not be a path to general intelligence. Is that what you’re saying?

That is what I’m saying. That we know how to build systems that look at data and make predictions or forecasts that infer things that we can’t even directly observe, which is remarkable. We do not know how to make systems that mimic intelligence in ways that would distinguish it from the systems or from humans.

I’ve had 100 guests on this show – and they virtually all believe we could/can, with your caveat about the timeframe, create a general intelligence, even though they all agree we don’t know how to do it. The reason those two things are compatible is they have a simple assumption that is: humans are machines, specifically our brains are machines. You know how the thought experiment goes… if you could take what a neuron did and model that and then did that a hundred billion times and figured out what the glial cells do and all that other stuff, there’s no reason you can’t build a general intelligence.

Do you believe people are machines, or our brains are purely mechanistic in the sense that there’s nothing about them that cannot be described with physics, really?

So I do believe that, with the caveat that we don’t necessarily understand all of that physics, necessarily today. I do think there is a biological and physical basis for human intelligence, and that should we understand it well enough, we could possibly construct something that’s indistinguishable. But we certainly don’t understand it and we may need to invent entire new fields of physics before we would.

Listen to this 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.

Voices in AI – Episode 96: A Conversation with Gary Marcus

Source: https://gigaom.com/2019/09/19/voices-in-ai-episode-96-a-conversation-with-gary-marcus/
September 19, 2019 at 03:00PM

[voices_in_ai_byline]

About this Episode

Episode 96 of Voices in AI features Byron speaking with author and psychologist Gary Marcus about the nature of intelligence and what the mind really means in relation to AI.

Listen to this 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 Bryon Reese. Today our guest is Gary Marcus. He is a scientist, author, and entrepreneur. He’s a professor in the Department of Psychology at NYU. He was the founder and CEO of Geometric Intelligence, a machine learning company later acquired by Uber. He has a new company called Robust.AI and a new book called Rebooting AI, so we should have a great chat. Welcome to the show, Gary.

Gary Marcus: Thanks very much for having me.

Why is intelligence such a hard thing to define, and why is artificial intelligence artificial? Is it really intelligence, or is it just something that can mimic intelligence, or is there not a difference between those two things?

I think different people have different views about that. I’m not doctrinaire about vocabulary. I think that intelligence itself is a multidimensional variable. People want to stuff it into a single number and say your IQ is 110, or 160, or 92, or whatever it is, but there are really many things that go into natural intelligence such as the ability to solve problems you haven’t seen before, or the ability to recognize objects, or the ability to speak or to be very verbal about it. There’s many, many different dimensions to intelligence. When we talk about artificial intelligence, we’re basically talking about whether machines can do some of those things.

You’re a provocative guy with all kinds of ideas in all different areas. Talk a little bit about the mind, how you think it comes about in 30 seconds or less, please. And will artificial intelligence need to have a mind to do a lot of the things we want it to do?

The best thing I ever heard about that, short version, is Steven Pinker was on Stephen Colbert. Colbert asked him to explain the brain in five words, and he said brain cells firing patterns. That’s how our brains work is there’s a lot of neural firing, and minds emerge from the activity of those brains. We still don’t really understand what all that means. We don’t have a very good grip on what the neural processes are that give rise to basic things like speaking sentences. We have a long way to go understanding it in those terms.

I tend to take a psychologist’s perspective more than a neuroscience perspective and say the mind is all of our cognitive functions. It’s how we think and how we reason, how we understand our place in the world. Machines, if we want to get to the point where they’re trustworthy, are going to have to do many of the things that human minds do, not necessarily in identical ways. It has to be able to capture, for example, the flexibility that human minds have, such that when they encounter something they haven’t seen before, they can cope with it and not just break down.

I know you said you don’t usually approach it from neurology, but I’m fascinated by the nematode worm who’s got just a handful of neurons. People have spent so long, 20 years in the OpenWorm project, trying to model those 302 neurons to make behavior. They’re not even sure it’s even possible to do that.

Do you think we are going to have to crack that code and understand something about how the brain works before we can build truly intelligent machines, or is it like the old saw about airplanes and birds [flying differently]? They’re going to think in a way that’s alien to the way we think?

I think it’s somewhere in between, but I’m also pushing towards the psychology side. I don’t think that understanding the connectome of the human brain or all those connections is anytime soon going to really help us with AI. I do think that understanding psychology better, like how people reason about everyday objects as they navigate the world, that might actually help us.

Psychology isn’t as much of a prestige discipline, so to speak, as neuroscience. Neuroscience gets more money, gets more attention. Neuroscience will probably tell us a lot about the nature of intelligence in the long term. That could be a long term of 50 or 100 years. Meanwhile, thinking about psychology has actually led to some AI that I think really works. None of it’s what we call artificial general intelligence. Most of the AI we have doesn’t owe that much to neuroscience, and if anything, it owes something to psychology and people trying to figure out how human beings or other animals solve problems.

Yeah, I agree completely with that. I think AI tries to glom onto things like neural nets and all of that to try to give them some biological tie, but I think it’s more marketing than anything.

I was about to say exactly that. I think it’s more marketing than anything.Neural networks are very, very, loosely modeled on the brain. I’m trying to think of a metaphor. It’d be like comparing a child’s first drawing to some incredibly elaborate work of art. Okay, they’re both drawings, but they’re really not the same thing. Neural networks, for example, only have essentially one kind of neuron, which either fires or doesn’t. Biology, first of all, separates the firing neurons from the inhibiting neurons, the positive from the negatives, and then there are probably 1,000 different kinds of neurons in the brain with many different properties. The so-called neural networks that people are using don’t have any of that. We don’t really understand how the biology works, so people just ignore it. They wind up with something that is only superficially related to how that brain actually functions.

Let’s talk about consciousness. Consciousness is the experience of being you, obviously. A computer can measure temperature, but we can feel warmth. I’ve heard it described as the last great scientific question we know neither how to pose scientifically nor what the answer would look like. Do you think that’s a fair description of the problem of consciousness?

The only part I’m going to give you grief about is that it’s the last great scientific question. I mean, as you yourself said later in your question, it’s not a well-formed question. Great scientific questions are well formed. We know what an answer would look like and what a methodology would be for answering them. Maybe we lack some instrument. We can’t do it yet. We need a bigger collider or something like that where we understand the principle of how you can get data to address it. [With] consciousness, we don’t really at this point know that.

We don’t know even what a ‘consciousness meter’ would look like. If we had one, we’d go around and do a bunch of experiments and say, “Well, does this worm that you’re talking about have consciousness? Does my cat? What if I’m asleep? What if I’m in a coma?” You could start to collect data. You could build a theory around that. We don’t even know how we would collect the data.

My view is: there is something there that needs to be answered. Obviously, there is a feeling of experiencing red, or experiencing orgasm, or whatever we would describe as consciousness. We don’t have any, I think, real scientific purchase on what it is that we’re even asking. Maybe it will turn out to be the last great scientific question, but if it is, it’ll be somehow refined relative to what it is that we’re asking right now.

Do you believe that we can create a general intelligence on some time period measured in centuries, even? Do you believe it’s possible to do that?

I do, absolutely. I’m widely known as a critic of AI, but I’m only a critic of what people are doing now, which I think is misguided in certain ways. I certainly think it’s possible to build a general intelligence. You could argue on the margins. Could a machine be conscious? I would say, “Well, it depends what you mean by conscious, and I don’t know what the answer is.”

Could you build a machine that could be a much more flexible thinker than current machines? Yes, I don’t see a principled reason why you couldn’t have a machine that was as smart as MacGyver and could figure out how to get its way out of a locked room using twist ties and rubber bands or something like that, which a current machine can’t do at all. I don’t see the principled reason why computers can’t do that, and I see at least some notion of how we might move more in that direction.

The problem right now is: people are very attracted to using large databases. We’re in the era of big data, and almost all of the research is around what you can do with big data. That leads to solutions to certain kinds of problems. How do I recognize a picture and label it if I have a lot of labels from other people that have taken similar pictures? It doesn’t necessarily lead you to questions about what would I do if I had this small amount of data, and I was addressing a problem that nobody had ever seen before? That’s what humans are good at, and that’s what’s lacking from machines. This doesn’t mean it’s an unsolvable problem in principle. It means that people are chasing research dollars and salary and stuff like that for a certain set of problems that are popular right now. My view is that AI is misguided right now, but not that it’s impossible.

Listen to this 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.