Friday, May 27, 2016

Interview with Hitoshi Harada, CTO/Co-founder of Alpaca

- What is your name
Hitoshi Harada

- What is the name of your company?
Alpaca

- Who are you?
I'm a co-founder and CTO of Alpaca. I bring more than ten years of experience in database core engine technology and data-driven applications.

- What does your company do?
Alpaca's mission is to democratize the institution-level digital asset management in capital markets such as quantitative financial analysis and trading automation using Artificial Intelligence.  We currently provide a product called Capitalico, which helps traders build automated trading algorithms without any lines of programming code by simply highlighting the similar patterns in the historical chart.  It has been very difficult for computers to interpret the complicated technical charts at the level of human beings, but thanks to the evolution of deep learning, we are now able to capture human interpretation of charts.  Typically, traders have to sit in front of the computer just to wait for the winning chart pattern to come, our CEO who has over 10 years of trading experience identified this as a heavily burdensome inefficiency. We resolved to solve this problem and now with Capitalico traders can get the same results but with unprecedented efficiency.

- What caused you to start your company?
I have been interested in new technologies that change the world, especially things that have happened in Silicon Valley where I am now.  My career has been around database technology and I have observed how much of the so-called Big Data hasn't been utilized enough due to the lack of capability to understand complex data.  When I saw the recent AI technology evolution, it was obvious that this emerging technology is going to change the world.  Two of my longtime friends had already been working around the technology, and it was clear to me that I had to start something.

- What makes your company an AI startup?
Our company started around image recognition and used to provide AI technology to solve image problems to big companies in the game and internet area.  There is a lot of excitement around recognition performance improvements made by AI lately, but each use cases was different, so we built a service called Labellio which we later sold to Kyocera, one of the biggest Japanese ICT companies. We then pivoted to Fintech and started this Capitalico service with similar technology and similar concept, but much more focused in the financial trading sector. Our company's philosophy is "be a human being", meaning we want to use AI to free people from those nonsense tasks.  Computers have made a lot of things automated but we don't think it's enough, and believe people should spend more time on more "human" tasks such as creative work.  As a consequence, it was natural for us to build an AI startup.

- Describe your company's AI.
Capitalico is a tool for financial traders to build automated trading algorithms by understanding the chart patterns.  Users just need to find similar patterns from historical charts, highlight and click “add.”  It builds a different AI model for each user since there are infinite ways to read technical charts and there is no single answer.  It is a very important concept for us and is inherited from the previous image recognition service to enable our AI to build a dedicated model for each particular problem.  We don't believe a ‘God AI’ exists as some people may.  Instead, our approach is that we should build AI to understand very particular problems that help the productivity of human beings.

- How do you measure and communicate the quality of your AI?
Within our space, there are typically two aspects with regards to the quality of AI.  One is of course the accuracy of recognition and the other is trading performance itself.  While it is important for our AI to accurately understand the way our users interpret charts, at the end of the day, our users care more about how much money our technology could make them.  So, it is important that we always keep a focus on make it as transparent as possible in both recognition accuracy and trading performance. We accomplish this by showing the prediction score overlaying the chart and calculating the backtest results including those metrics such as Sharpe ratio and drawdown.

- Can you share something awesome that your AI has done or that you have been able to do with your AI that would surprise most people?
Chart pattern recognition has been one of the challenging problems in this space for more than two decades.  Some of the previous work has developed a hand-crafted way to find the known patterns of one time sequence value of closing price, but the real traders are looking at more complicated charts with many indicators on top of the raw prices, and the patterns are a combination of known simple patterns plus the market momentum.  Our AI has proven results to find similar patterns considering all of those complicated configurations.

- When you talk to investors, do they care about your AI or do they care that you are solving a business problem? How about customers? How about the media?
Investors are often less concerned about the actual technology, instead, they focus on how much we can make. However, it is also true that investors are knowledgeable in the value of solving important business problems.  In order to build a stable business around a startup, we do need the technology strength so that others cannot easily catch up.  That's how investors look at us.  Customers are similar in that they care whether we can solve their problems or not.  Customers don't even care if our business is going to be stable or not.  Sometimes the cool technology sells itself, but we know that's not the core.  But among everyone, it is the media that is most enthusiastic about our technology.
- Why is there so much excitement about AI startups?
Because AI will change every business for sure.  And also because it is one of the hardest problems in history.  It's not something everyone can do, but if you can do it, it will be huge.  It's not just about technology but it's about what kind of problems you solve using what kind of AI technology.  So more and more people jump into the space trying to be the one, and more and more people watch what's going on in this space, and that's why there is so much excitement.

- Where do you go to get information about AI startups?
Today's AI space is so hot that you can find general trends by reading news sites.  For more technology related knowledges, I follow a couple of active twitter and facebook accounts such as Deep Learning Hub and AI technology community.  The thing is, the community is growing so rapidly and new technology comes up literally everyday, so social media is a great tool to stay on top of the evolving environment.

- Make a prediction about the future of AI startups.
I have heard many people saying that AI will change every business, and I agree with that, but it's not clear yet how it will change.  Some people are trying to build a big AI system that understands everything, but there are more people like us who think that this isn’t the goal.  At Alpaca, we believe that AI should enhance the professional's skill to solve problems in particular domains, and the way AI solves each problem can and should be different.  In the human world, there is no single answer to life’s challenges.  AI will follow the same rule.

Saturday, May 21, 2016

Wednesday, May 4, 2016

AI Startups Conference: Schedule

We are running an AI Startups Conference in San Francisco on May 25th.

We have a stellar agenda with extraordinary speakers.

8:00 AM Registration
9:00 AM Bo Morgan DreamWorks Animation A Cognitive Architectural Map of AI Startup Ideas
9:30 AM Sarah Austin Broad Listening Going from Soft to Hard in 30 Minutes 
10:00 AM Matt Johnson QC Ware Quantum annealing computing for optimization and machine learning
10:25 AM Break
10:40 AM Cory Kidd Catalia Health Current AI + Robotics Opportunities in Healthcare
11:10 AM Eric Danziger Saimir.ai Simulating Worlds to Train AI
11:40 AM Alex Kern Pavlov Deep Learning at Scale
12:10 PM Celeste Baranski Numenta Reverse Engineering the Neocortex
12:35 PM Lunch
1:20 PM Philip Thomas Staffjoy Decision Algorithms in Production
1:50 PM Ian Foley acuteIQ Using AI to find customers for the financial services industry
2:20 PM Farzan Fallah Idelan Bringing Smart Watches to the Mainstream using Computational Linguistics
2:50 PM Alex Jaimes AiCure Revolutionizing Healthcare: Mobile AI to Improve Health Outcomes
3:15 PM Break
3:30 PM Rand Hindi Snips Building an Artificial Intelligence with Privacy
4:00 PM Alex Chan Datanovo Artificial Intelligence Intellectually Disrupts Patent Litigation
4:30 PM Philip Low NeuroVigil Authentic Intelligence: Harnessing the Brain’s Whispers with Advanced Algorithms

Monday, April 25, 2016

AI Startups Conference: Confirmed Speakers

(Thanks to Jenny Liu for collecting this information.)

The AI Startups Conference will take place on May 25 in San Francisco.

Here are the confirmed speakers:

Monday, April 18, 2016

AI Startups Conference: Call for Speakers


Join Celeste Baranski and Eric Larson as a speaker at the AI Startups Conference which will take place on May 25, 2016 in San Francisco.

We expect this informal event to become the place for people to go to learn and share information about AI startups.

We are now soliciting speakers for 25 minute speaking slots. To submit a talk, please send a title, abstract, and bio by Friday, April 22nd to michael.delamaza@gmail.com.


Monday, April 11, 2016

Daniel Dennett, AI Philosopher

Daniel Dennett is one of the foremost AI philosophers. 


Is it possible in principle to make such a robotic bird? I think possible in principle.
What would it cost? Oh much more than sending people to the moon.  It will dwarf the Manhattan Project. It would be a huge effort and we wouldn’t learn that much.

My sense is that the trajectory of philosophy is to work on very fundamental questions that haven't yet been turned into scientific questions. Once you get really clear about what the questions are, and what would count as an answer, that's science. Philosophy no longer has a role to play. That's why it looks like there's just no progress. The progress leaves the field. If you want to ask if there has been progress in philosophy, I'd say, look around you. We have departments of biology and physics. That's where the progress is. We should be very proud that our discipline has spawned all these others.

The intentional stance is the strategy of interpreting an object as an agent with beliefs, desires and rationality. You can adopt the intentional stance towards a person, an animal, a thermostat – which ‘wants’ to maintain a certain temperature, and regularly updates its ‘belief’ about what the current temperature is – or, more interestingly, a chess-playing computer, which ‘knows’ the rules, has true ‘beliefs’ about the positions of the pieces on the board and ‘wants’ to win.

Monday, April 4, 2016

Interview with Rachel Law, Founder of Kip


Rachel:
I'm Rachel Law, I'm one of the founders at Kip, I'm the CEO, and I'm very happy to be a part of your interview today.
Michael:
Great. Can you tell me what your company does?
Rachel:
Kip is an artificial intelligence chat-bot that helps you and your team with group shopping. We focus specifically on group shopping, both on enterprise and consumer side. We define group shopping as shopping with 3 or more people.

For instance, when an office needs to order lunch delivery, there’s normally one person, the office manager who does all the ordering. They have to go around the office asking people what they want for lunch, whether they want brown rice, or white rice or whether they want soup or salad. It’s tedious, time-consuming and additional responsibility for the office assistant.

Kip is assists these organizers to make their lives easier. They can just ask Kip, and Kip will ping everyone in the team, collect and coordinate the orders and send the office manager a single checkout link.

At home, there's normally one main caregiver. They manage the spouse's needs and the children, and maybe they have elderly parents as well. All of the responsibilities are directed to this one person. We came up with Kip to assist these people who have too many demands.

Michael:
What's your background?
Rachel:
I first started working in art. I was one of the early artists who started working with digital art back in 2011, and physical computing, so I'm quite familiar with robotics. Afterwards, I did my masters in design and technology. For my thesis, I built a program called Vortex that lets users swap their meta-data across networks so they could pretend to be other people or invent new identities to get better shopping deals.

This project went nation-wide. It was featured in Ad Age and KPCC California radio, as well as some other places. I worked a bit in advertising and then my co-founder Alex and I started Kip together.

My background is mostly e-commerce. I was running an e-commerce site since I was a teenager. I did it for 10 years. We had 12 million daily visitors and $6000 in transactions a week. When we formed Kip, the data corpus came with me so we could train our AI, and some of the profits helped start the company.
Michael:
So what caused you to start your company?
Rachel:
A lot of factors. Firstly, we felt that we had something very interesting and a different approach to artificial intelligence. We saw that a lot of AI companies were very very focused on what we call, “concierge services” and these AI were basically made for a small subset of wealthy people.

Second was that some of these AI startups were focused on starting with humans first.They had call centers of people running their artificial intelligence. So it wasn't really artificial intelligence as much as human automation. In future they would use it for machine learning training, but at present it was all human powered.

We were like, "Well maybe we don't have to do that. What's the lowest modicum of intelligence that people were willing to use for group shopping?”  Shopping is a great way to validate an idea, because when someone is willing to put their money, it means that it works.

So we started with a very stupid kind of chatbot. Barely any intelligence, no reinforcement training, no training, no nothing. Just simple simple rules. Then we put it out there, and people started using it. It was like, "Okay, so people don't actually care that much about intelligence, as long as it works." That's when we started developing and refining. How do we optimize the situation so that it can get usefully smarter and smarter? That's when the artificial intelligence started kicking in.

This was driven by people who would give us all kinds of queries and shopping requests. If you're just using a very standard library, you're going to run into trouble because conversations use short and truncated sentences compared to paragraphs in doc2vec. That's where we started doing what we call intent optimization.

Shopping is an interesting thing. Most of the time, the purchase decision comes after much evaluation. The point of sale comes after you browse the product, you've gone through the evaluation process, and then at the a sale. It's the opposite of a conversation. Most of the time a conversation is when you ask a question, the first line of answer is the most important piece of information, then you get more and more detailed as you go along.

That's what they teach in news journalism, it's an inverted pyramid for information.

How do you reconcile the fact that people normally shop after much evaluation? At the same time, people expect the first answer by Kip to be the perfect one. What we did was start feeding data for reinforcement training. Let's say you want to buy chocolate:
 
You: "Hey, I'm looking for chocolate."
Kip: "Here try these three chocolates, which do you like best?"
You: “I like the second one, but do you have it in dark chocolate?”

Kip will show you more choices, and you'll go: "I like the third one. Do you have it under ten dollars? I like the 1st one, do you have it gift wrapped for me?"

Maybe it takes a total of eight steps for you to complete the purchase, after much evaluation. What we do is we take the customer's steps in the conversation, and then we feed it back to the system for training. So that when someone else searches for the same term, "chocolate" on the same day and they have similar preferences to you, we can optimize the result higher. Instead of taking eight steps, it’ll take only four steps in order for you to find what you're really looking for. It's a kind of best-match system where we are using people's behavior to give the best result. The same way that people use the rating system, except that the ratings system is calculated for a personal match.
Michael:
Can you say something more about your company's AI and how it works?
Rachel:
Yep, I guess it depends what kind of AI you're looking at. We've been working on this for the past two years. It's kind of broad, if you could be a little more specific on the kind of AI you're specifically interested in, I could tell you how it works.
Michael:
Sure, let's talk about NLP first.
Rachel:
Okay. On the NLP side, one of the things we first wanted to see, "Okay, how do we narrow down user language to exactly what we want it to?"

NLP is a boundless problem. It's one of those things that will never be solved because every time you're trying to build something, someone will have come up with a new slang, a new dialect, another idiomatic language factor. They key to solving NLP problems is to make sure that our NLP problem is super super clear, and has very clear problems to solve.

For us, what we wanted to do was fulfill a sales transaction. Sales transactions, is a very structured kind of conversation. The two main driving factors in the sales conversations is price and availability. Most people will ask are questions centered around price and availability. So that's the first thing you want to consider. The second part is how will they ask these questions? Depending on which platform you're launched on, the way a person will phrase the question will affect how the responses that Kip can give. In language, it’s known as a linguistic register.  Your question and answer system needs to be adapted, designed, and centered on the platform you're launching it on.

For instance, if you're launching it on Slack, the kind of questions that come in are normally formally phrased, very technical and relating to product information because the user's more interested in a price comparison or a product review. "Is this the best monitor that we should use for everyone in the company?" The users on Slack are mainly enterprise, so the type of purchase is a corporate purchase.

Whereas if you launch it on something like Kik, you'll see a lot more slang and idiomatic phrasing that are not related to language as we think of it. We started building our universal emoticon translator for this reason. We have a system that translates emoticons or emojis, across the platform. So you can actually use emojis to search for things. Instead of typing a question, you can just send an emoticon or combinations of emoticons, and it would work as well.

We found that to using emoticons is a lot easier for some people because they’re used to thinking of these as language. So instead of text or typing, they would send these to their friends. We adapted that form of communication into Kip. That’s the idea of language, the easiest and most common types of communication.



Then on the machine learning side, you take all these chat conversations, especially  things that we don't understand. These flagged things are tested at the end of the day by me or by my co-founder Alex. Then we feed it back into the system so the system improves.
Michael:
How do you measure the quality of the NLP?
Rachel:
That's a very good question. We don't  measure it like you'd use for Image Net for accuracy. We have a baseline, which is what we originally started with when we had a stupid system, that it just gave generic responses for everything.

For us, the accuracy is how many things don't get sorted into the response buckets. Each bucket is like a micro-service. When a user initiates a conversation with Kip, all of the users' conversations get directed into different buckets.

We measure is how many things don't fall into the bucket, and whether they went into the right bucket. If it doesn't fall into the bucket it means that it's something that Kip does not understand, if it goes into the wrong bucket it means that Kip has a misunderstanding. One is an ignorance, the other is poor or not enough information.

Does that make sense?
Michael:
Yep. Can you share something awesome or surprising that Kip has done that has impressed you or one of your customers?
Rachel:
Probably the emoticon and image search. Most people don’t consider these things as important or part of language, but it’s very interesting because language is constantly evolving. As they say, an image tells a thousand words.

When I say emoticon search, I really mean it as a combination of a text, text as in Roman alphabet, pressing I'm sad, then you give a funny emoticon. Normally that would be considered a conflict of two ideas. Because why is there a funny picture next to an I'm sad image? But Kip can actually parse that. If you actually go on Kip and try to search combinations of emojis, you would get results that are relevant for what you're searching for.

If you think about it, that's where language is moving toward. People are using emoticons as a way to express themselves. You look at Instagram, half the bios are all full of emoticons.
Michael:
When you talk to emoticons, do they care about the AI, or do they care about the fact that you're solving a business problem?
Rachel:
It depends what kind of investor you're talking to. I know it sounds like a terrible answer, but it really does.

Most conservative investors generally does not invest in AI, simply because they know that AI is a black hole, where you can keep throwing money and you might not see anything returned. Facebook is already facing a bit of this, and so is Google, where they've realized that you can be working decades, and pouring all their resources into this and it might not actually turn out to be anything.

In that sense, because we have a very specialized form of artificial intelligence that's when they start caring. The fact on the day that we launched, we were already generating revenue helps. We do a very specialized form of artificial intelligence. So we don't build robots, we don't make driverless cars, we don't care about philosophical questions. Sorry. We don't really care about what's the meaning of life, who should live or who should die. These are the kinds of questions people like to ask general AI's. We're not interested in taking the Turing test either, we don't think it's not that useful. We only do one thing, and we do it well. We help people with group shopping. That's kind of our focus.

Having a very clear focus means that we can develop a very specific AI, that is intelligent in it's own way. That makes it interesting to investors. There's that very clear benchmark of what we're going to achieve. It is a very clear benchmark of what is considered intelligent. That's something that people rarely think about.

What would you consider as intelligent? How much intelligence is necessary? What kind of intelligence are we creating? You need benchmarks. If not, you're going to get a team of data scientists and they're not going to know what they're measuring for.
Michael:
Why is there so much excitement about AI startups?
Rachel:
I think there's a lot of interest in artificial intelligence startups because they see the future of work being automated. On a very practical, capitalist sense, I think that investors are interested in artificial intelligence because it's going to be the way forward the way Henry Ford revolutionized the assembly line.

Except instead of using human workers on the assembly line, they're using artificial intelligence on the assembly line. Which means that a large portion of work can now be automated, and goods will be much cheaper to produce. Because an AI doesn't need to eat, doesn't need to sleep, doesn't require healthcare, and all that kind of factors.

The other thing about AI, is that artificial intelligence's aren't as artificial as people think they are. At the end of the day, they still trained on human data sets. They're artificial in the sense that, yes they're not nature-born and they're not human, but they're humanly intelligent. They're an aggregate of all of our intelligence, specifically pointed on one field. You can think of them as a kind of super-human collective knowledge point.




Michael:
Where do you go to get information about AI startups?
Rachel:
Right now,  there’s so many AI startups that you don’t have to find it. It’s in the news every other day. There’s probably there's another new AI startup on Angel List as we speak. AI is in the news constantly. Finding an A-I startup is not difficult. It's finding who's doing things that are interesting, and who's doing things that are actually useful, and who is just creating vapor-ware or fear-mongering.

There's so much news about AI,  that it’s overwhelming. That in itself could be one AI, an AI that can curate useful news. Everyone posts mis-information now.

On our side, we have an AI meetup group called Deep Learn NYC. We host networking events and invite industry speakers every few months to talk about practical applications of machine learning and deep learning.
Michael:
Make a prediction of AI and startups.
Rachel:
In the future ... AI startups or AI?
Michael:
AI startups, AI and startups.
Rachel:
Well that's two different questions actually. In the case of artificial intelligence, it’s inevitable. We are going to have a future with A-I whether you are on board or not.

The reasons for this beyond just technology. We as human beings have constantly tried to create things in our image. It's a kind of compulsion almost. We're fascinated with the things that we can do. We are fascinated with whether we can create a version of ourselves, and challenging our own identity is human beings. It's almost like a God-like thing to do. We try to create artificial intelligence.

Whether you are on board or not on board the artificial intelligence train, it will happen. It's just a matter of time.

In the case of AI startups, if you're not already on an AI startup and you're thinking of creating one, unless you have a very very unique angle and approach or proprietary data it’s not going to be easy. It would be very difficult to get in the game. Now that everyone is talking about it, and it's now an open field. If you started a few years ago, now you’re hot and you have a competitive lead. If you start now, you’re not only behind but you’re competing with everyone who read yesterday’s news. The investors are almost over it. It's like the stock market. The moment laypeople know, "Okay it's time to buy Apple shares," it means that large portion of the momentum is already gone. That whoever started it two years ago, they've already scooped up the resources. If there's already investment, it's probably already committed to somebody else.

It's going to be very very competitive, similar to 1950s manufacturing. A lot a lot of people will try, and then a lot of people will fail, and there will be a market consolidation probably 5 - 6 years down the road. You’ll have a handful of companies who are entirely dominant in one field of AI. There will be an AI startup for healthcare that will manage all aspects of health care. There will be an A-I startup in banking that will manage all the predictive trading, and so on.

You will get that kind of market consolidation simply because it doesn't make sense to have multiple A-I's companies. Because of artificial intelligence are best in large quantities of data, and having too many AI companies is a duplicate of resources.

If everybody has a small data set, then it makes sense one competitor, whoever is going to win it, to acquire all these smaller data sets so that you get a very very large user base, and then you get a very very large access to information so that it actually makes artificial intelligence worth it. Then you can really scale and be extremely defensible, because it would be difficult to duplicate.
Michael:
Great. Thank you so much, I really appreciate it.