Are Machines Already Smarter Than Us

Intelligence has always been an amazing topic for conversations: whether it’s about discussing what it is precisely or other people’s lack of it, it never fails to provide food for thought. Now with the rise of artificial intelligence, we have one more topic to debate, make predictions about and feel excited (or threatened) by. So far we have taught machines to draw, drive cars, write poems, beat humans playing Go, and even chat with us. AI is obviously getting smarter, but is it already smarter than us?

In 2002, Mitchell Kapor, co-founder of the Electronic Frontier Foundation and the first chair at Mozilla, and Ray Kurzweil, author, computer scientist, inventor and futurist  who works for Google, established a $20,000 wager. The bet was over whether a computer would pass the Turing Test by 2029. They called it “A Long Bet.” Kapor bet against a computer passing the Turing Test by 2029, while Kurzweil believed it would happen. Has the bet been resolved in 2018? Let’s take a deeper look.

AI: The Origins

Let’s go all the way back to ancient history. Just think about all the myths and stories about artificial beings who get their consciousness by a divine power. The seeds of AI were planted by philosophers who tried to describe the process of human thinking as the mechanical manipulation of symbols. In 1308, the Catalan poet and theologian Ramon Llull published Ars generalis ultima (The Ultimate General Art), which perfected his method of using paper-based mechanical means to create new knowledge from combination of concepts. Following that in 1666, mathematician and philosopher Gottfried Leibniz published On the Combinatorial Art , which proposed an alphabet of human thought and argued that all ideas are nothing but combinations of a relatively small number of simple concepts. All of this culminated with the invention of the programmable digital computer in the 1940s. So scientists had the base to start discussing the possibility of building an electronic brain.

The term “artificial intelligence” was coined in a proposal for a “2 month, 10 man study of artificial intelligence” in August 1955 in Dartmouth College. The workshop involved John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Claude Shannon (Bell Telephone Laboratories) and  Nathaniel Rochester (IBM).  The workshop took place in 1956 and is considered the official birth of the new fied. In 1959 Arthur Samuel coined the term “machine learning” when he was trying to program a computer to learn to play a better game of checkers better than the person who wrote the program.

AI: The Test

In the far 1950, Alan Turing developed an actual test, which would help determine a machine’s ability to exhibit intelligent behavior compared to that of a human. The test involved a human evaluator who would judge natural language conversations between a human and a machine designed to generate human-like responses. The judge will be aware that a machine is involved. The conversation would be limited to a text-only channels such as a computer keyboard and screen. If the evaluator cannot reliably tell the machine from the human, the computer passes the test. In this test there are no right and wrong answers- just answers close to human speech.

The test has been introduced in Turing’s paper “Computing Machinery and Intelligence.” The first sentence states: “I propose to consider the question, 'Can machines think?’” But thinking is too difficult to define so Turing replaces this question with another: “"Are there imaginable digital computers which would do well in the imitation game?" Turing believed that the new question can be answered.

AI: The Bet

In 2014 a computer successfully convinced a panel of judges that it was human. Thus it passed the Turing Test. The test was held by the University of Reading and the organization announced that for the first time a computer passed. The computer’s name was Eugene Goostman and it tricked the judges 33% of the time. But did it really helped Kapoor win the bet so that Kurzweil owes him $20,000?

Yes, Eugene Goostman passed the Turing Test and fooled the judges more than 30% of the time in their five-minute conversations. No, Kurzweil doesn’t owe Kapoor $20,000. Yet. The bet had explicit rules and the experiment at the University of Reading didn’t meet all of the listed criteria. For example, to help Kapoor win, a computer needs to have a conversation of at least eight hours, which means the computer will need to convince two out of three judges.

But why should Kapoor be worried?

Machines are getting better at everything we are teaching them to be. What makes machines smarter? Seth Shostak, the former director of the Search for Extraterrestrial Intelligence Institute (SETI), believes that we can build computers that can beat humans at specific tasks (like winning the game Go). The machines can’t do everything better, but he thinks that eventually we will design AI that is as complex and intelligent as a human brain.

"But the assumption is that that will happen in this century. And if it does happen, the first thing you ask that computer is: Design something smarter than you are," says Shostak. "Very quickly, you have a machine that's smarter than a human. And within 20 years, thanks to this improvement in technology, you have one computer that's smarter than all humans put together."

AI is learning quickly. Just one recent example is The AI Hacker: in 2016 the Darpa Cyber Grand Challenge hosted the first hacking contest between a pit bot against bot. Designed by seven teams of security researchers from across academia and industry, the bots were asked to play offense and defense, fixing security holes in their own machines while exploiting holes in the machines of others.  

Not to mention the infamous story which the more dramatic amongst us (or the Black Mirror fans) saw as the beginning of the reign of AI over humans: that time when Facebook had to shut down two chatbots, just because no one understood what they were talking about.  The researchers didn’t seem to worried about it. "There was no reward to sticking to English language," Dhruv Batra, Facebook researcher, told FastCo. "Agents will drift off understandable language and invent codewords for themselves.”

In the meantime Google is feeding its AI with unpublished books and, in return, the AI is composing mournful poems. And if you’ve played with the AI-powered tool that Google released in 2016, you’ve actually helped it learn how to draw. The program is called Sketch-RNN and it draws pretty well...for a machine. The drawings are basic, but they are not what is important. The method used to create them can be quite useful. It is paving the way for AI programs which can be used as creative ads for designers, architects and artists.

We, on the other hand, have focused on the image recognition abilities of AI. A while ago we asked you to play in the Clash of Tags. Players were presented with two sets of images for a given text tag and had to vote which set was describing the image better. It turned out that machines were almost as good as humans. So for now, the result is even. But the battle is not over.

Human: Intelligence?

So what is intelligence? According to Einstein, “The true sign of intelligence is not knowledge but imagination” Socrates said, “I know that I am intelligent, because I know that I know nothing.” Philosophers got created in the ancient search finding the true measure of intelligence and meaning. Today neuroscientists try to answer questions about intelligence from a scientific perspective. It is widely accepted that there are different types of intelligence—analytic, linguistic, emotional, to name a few—but psychologists and neuroscientists disagree over whether these intelligences are linked or whether they exist independently from one another.

In the meantime, computers will be getting smarter. Yes, they can process certain kinds of information much faster than any of us can. Computers learn more quickly and narrow complex choices to the most optimal ones. They have better memories and can analyze huge amount of information. Computers can calculate and perform tasks without stopping.  On the other hand, humans are better at making decisions and solving problems. Humans are capable of experiencing life.  We have creativity, imagination and inspiration. Computers replicate tasks, but they can’t create. Yet.

Imagga Featured Hack: Hipster Bar

Hipster bar is where only hipsters are allowed! How do you reinforce that? With a physical doorman who’s job is to ruthlessly send back guys without beards, or, in the case of the Max Dovey’s project - using Imagga’s image recognition technology. The hipster bar was open to the public for the duration of WdW Festival 2015.

Let’s get into the details of this quite unique usage of Imagga’s powerful image recognition technology. To enter the bar, you need to stand in front of camera that snaps a photo of you and then sends it to Imagga servers. Then the tech analyses your look and as result returns how certain the system is you are a hipster.
If you are found over 90% hipster, the door of the bar will open and you can join great company of people that are hipster enough.

Hipster is quite loose term and usually is used to describe a subculture of people who attempt to keep up to date with the latest trend and remain 'hip'. These are men and women in their 20's and 30's that value progressive politics and independent thinking, and often have appetite for art and indie-rock & counterculture. Of course being hipster includes certain look - thick rimmed glasses, tight-fitting jeans, old-school sneakers,  side-swept bangs and beards (men only).

Max Dovey, an artist from Rotterdam, who initiated the project, sourced thousands of images of hipsters to be used by our team to build a special hipster deep learning mode.  The specific classifier was able to easily distinguish between snaps of hipsters and all the rest. Here’s how it actually worked:

Have another crazy idea? Don’t hesitate to try it out - with our custom training only the sky is the limit... if you are hip enough ;)

Can The Machine Beat Humans in Image Recognition

Clash of Tags - image recognition vs humans

For far too long image understanding has been considered too complex for the machines to deal with. It takes years of training for the human brain to build links between the visible and connect it to concepts of shapes, colors and objects. Even though neural networks were invented couple of decades ago and were considered huge step into machine AI, what lacked was computing power. With the advance of GPU computing, new opportunities were discovered, algorithms were reinvented so machine and deep learning are back on the table.

The machines are powerful enough now to grasp the world almost as good as a 3 years old kid. A prerequisite for neural network to work well is a clear, representative data that will make the outcome results more precise and accurate. Huge efforts to collect and classify the images of the world were undertaken in the last couple of years. Are the machines ready for a battle then?

At Imagga we take that challenge seriously by building an intelligent image recognition technology that can teach the machine to understand basic daily life objects, comprehend concepts and eventually deal with complex pictures, where lots of background information needs to be taken into account in order to be interpreted properly. It’s challenging task but we love what we do.

With that stated, we are ready to set the stage for an epic battle, the battle of the century - machines vs humans. To some it might sound funny, unrealistic, pretentious, but it’s coming. At least now in a form of a cool game, done with love by Imagga and Algolia.

We’ve called it Human vs. Robot: Chash Of The Image Tags. You will be taking central role of judging who tags better - the human or the machine.  You will be presented two sets of images for a given text tag and need to vote for the set that better represents the concept of the text tag. As every good judges you will need to be unbiased and make up your mind only on the facts, so you will not know which set was tagged by humans or respectively by machines. You will get five rounds to decide and pronounce a winner. Of course you can play as many times as you wish, and even invite your friends to try it out and have fun.

The game is made possible by the joined efforts of Algolia and Imagga. Algolia is building powerful search technology for exploration of large data sets. Algolia’s hosted search API delivers instant and relevant results as you type your search query. Imagga’s part is to provide the automated machine tagging of all the images you will be seeing in the search results.

It might be just a game, but the real idea is to demonstrate how powerful machine recognition is nowadays. It can really replace or at least greatly assist people in the process of tagging photos - it’s much faster, more cost effective, most of the time - more consistent and even more precise than human tagging. This empowers a lot of use-cases in stock photography, digital asset management, advertising, cloud storage and photo sharing that are otherwise not feasible or even not possible with human tagging.

Why don’t you play and judge for yourself Clash of the image tags!

Machine Learning Meetup in Sofia

ML Meetup Sofia

Three year ago when we publicly talked about machine learning, deep learning, convolutional neural networks and AI not many people were getting it. It was hard to explain what all this is about. Things have changed, and for good.

Last week we’ve invited a bunch of people to Machine Learning meetup. The first in Sofia. 60 people attended and it was awesome. It’s awesome to see so many people interested in AI and machine learning. And they were getting it. We are sure machine learning will be widely adopted in many tech verticals  in an year or so and are proud to be helping Bulgarian AI/ML community to exchange ideas and grow.

Judging by the number of people and cases that has been discussed, lots of startups are already exploring the power of machine learning in various industries - e-commerce, bitcoin landing, real estate, to mention few. It’s still the early days of ML community in Sofia, so we’ve started with some basics. Judging by the variety of the questions after our short intro presentation, next editions of Sofia Machine Learning Meetup will be quite geeky and interesting.

What we are after in 2014 - democratizing machine learning as a service and applying it in practice

2013 was such an exciting year for us! We partnered multiple photo-related hackathons (Seedhack 3Startup Weekend Mobile Sofia and Photo Hack Day Menlo ParkPhoto Hack Day BerlinSeedhack 4; plus one more coming in a less than two weeks - Photo Hack Day Tokyo!) and created a lot of friends there.

We also closed our seed round from LAUNCHub and several angel investors, extended our team with two great software engineers, visited several leading industry and technology conferences (Tech Crunch Disrupt BerlinWeb Summit DublinMicroStock Expo BerlinleWeb Paris), and made a major leap in our core technology:

Auto-categorization of images for different use-cases
Auto-categorization of images for different use-cases

We on-boarded new customers and partnered with two great image processing and management services - Blitline and Cloudinary that now offer our smart cropping as part of their feature sets (the latter, still in private beta).

Now 2014 has already come, and it's time to roll out several awesome products, enabled by our recent technology advancements. There is a huge hype around A.I. and machine learning these days. But for us this isn't just a hype... it's what we've been working on hard in the last year. And we want to bring this to the people (and start-ups, and companies), empowering them to take advantage of machine learning for better understanding and organization of their imagery in the easiest possible way.

We are aiming to keep the lean/iterative approach so we'll definitely rely on your active feedback, ideas, and inclusion in the process. Happy to share that we already have multiple successful pilots with customers taking advantage of our machine-learning-based auto-categorization, as in the example image above, and some more customers in the pipeline. Now it's your turn! If you are interested in auto-categorization and you think it can help your image-centric businesses or projects (and believe me, it will help them for sure) give a try to our auto-categorization "playground".

Mobile is also going to be bigger and bigger and this is something we are definitely interested in as the development of our smart photo organization app Sliki proves. Sliki was recently selected to join the AppCampus programme so we'll release it on Windows Phone first (which is a great platform for development BTW), but we also conduct some closed tests on Android. Sign up here and we'll keep you updated on how Sliki goes. Funny enough, the (hardware-enabled) future seems bright even for complex computer vision on mobile as explained in this video.

All in all, It's a really exciting journey and you are more than welcome to join it as an advisor, investor, employee, partner, customer or just a friend :)