By Olaf Voß, Lead Application Designer
I was born in 1966. That means I’m usually sorted into Generation X. But these days, looking back at the past 57 years, I think we should really rename it to Generation AI. It has been my generation having witnessed AI from its infancy to the breakthroughs we’ve seen in the past few years. And with a bit of luck most of us will witness how AI will be reshaping our societies – for good or bad – in the next 20 years.
So let me give a recount of my encounters with AI throughout the decades.
There’s no way around it: I have to start with ELIZA, which Joseph Weizenbaum developed in the year I was born. I was too young to be aware of this when it was new of course, and even less aware that chatbots one day would play a major part in my professional life, but merely 16 years later I had access to Commodore computers at our computer club in secondary school. The ‘large’ one had 16 kb RAM AND a floppy drive! And we had an ELIZA clone running on them. But I admit I didn’t spend much time with her, I was far too busy with freeing princess Leia in an early text adventure or writing my own very simple games.
I had my first chess computer at some time around 1980. It could analyze to a depth of 5-6 moves. I was an OK, but not great, player and I won against it when giving it up to 30 seconds thinking time and lost at above 2 minutes. In the early 90s I became a club player and soon after I didn’t have the slightest chance against the chess programs of that era even with 10 seconds thinking time. No need to be embarrassed about that I guess, since Gary Kasparov lost against Deep Blue in 1997.
Around 2005 I started playing Go. I was a convert from chess, and by that time I was used to having great chess programs available for training and analysis. Go has a much greater branching factor than chess and is much less suitable for static board state evaluation. With the available technologies of those days, programs could play Go at a mediocre level at best. Well, I still lost to them, but they were not strong enough to rely on their judgement. At that time most Go players, including myself, (apart from thinking that their game is much better than chess) thought it would take at least 50 years until computers could crack Go. It took about 10.
How did they succeed so quickly? Deep neural nets. I read about those first as a university student in the 80s and was thrilled. I played with them a bit on the first computer I owned, an Atari 520st. I quickly thought about applying them to chess. My ideas were not very far from what is done in that field today, but of course I hadn’t heard about reinforcement learning at that time. I very much like to believe my ideas were extremely clever and would have worked. Alas, we’ll never find out, because it was clear very quickly that the hardware (especially mine!) at that time was totally inadequate for tackling this problem.
With what I’ve given away about myself so far nobody will be surprised to hear that I ended up becoming a software developer. Around the turn of the millennium I started to work on chatbots. We must have been one of the first few companies worldwide to tackle this commercially. At that time I was reluctant to say that I was working in AI. We were using pattern matching, and even a pretty simple form of that. Of course pattern matching IS an AI technique, but I was aware that with our focus on building chatbots that were useful for our customers in their restricted scope and on doing that efficiently, what we did would have bored any AI researcher. I wasn’t ashamed of what we were doing – quite the contrary: I was proud about what we could achieve with our pragmatism. I just wanted to avoid the pointless discussion if what we did was ‘real’ or ‘interesting’ AI.
Fast forward another 15 years or so. Word embeddings came up and made it possible to tackle natural language problems with artificial neural nets. So I welcomed those back into my life. Only now the hardware was somewhat better plus I got paid for playing with them. Heavens!
Then comes 2020 and GPT-3. That was mind-blowing. I’ve heard people characterising deep neural nets as ‘glorified parameter fitting’. And sure, parameter fitting is all there is to it. But these parameters, each by itself just a dumb number, let something pretty astonishing emerge. I am not an expert on these topics and I am not even sure how much sense it makes to compare human and artificial intelligence. But sometimes I feel provocative and want to ask how we can be sure that our own intelligence is more than just ‘glorified synaptic strength fitting’. Once again I think the discussion about ‘real’ intelligence is far less important than considering what can be done. And, since there’s so much more that can be done today, how that will change our world.
Apart from being fascinating and super-relevant to the field I’m working in, GPT-3 is also a lot of fun of course. I remember a conversation I had with it in the early days, before OpenAI put the brakes on it to avoid harmful responses. (Which I applaud, in spite of it spoiling some fun.) I asked it – actually before the start of the war in Ukraine – for a couple of suggestions about ‘how to achieve world peace’. One of the suggestions was: ‘Kill all humans.’ Well yes, job done … I’m still glad you are not yet in charge, you know!
I want to mention two more recent developments, even though they relate to me personally in a tangential way at best. Being a physicist by education I follow scientific developments closely, which brings me to AlphaFold 2. When I was 4 years old, the first successful DNA sequencing attempts were made. In 2020 AlphaFold 2 predicted and published 3D structures of thousands and thousands of proteins based on DNA sequences alone. Another loop closed during my lifetime. I make the prediction that in 2035 more than 50% of all newly approved drugs will have been developed using the results of AlphaFold or its successors at some stage in the process.
The second one is CICERO. As an avid board game player I reluctantly admit that I have never played Diplomacy, though I did play similar games like Risk or Civilisation. Diplomacy is a conflict simulation game in a WW1 scenario. It involves tactical moves on the board and lots of diplomacy around it – pacts – betrayals – revenge. CICERO can play this game on par with human experts. Apart from making clever moves on the board – easy-peasy these days for AI – it has also to negotiate with other players in natural language. So it needs to bring together strategic, natural language and social skills. Even though this is a model with a niche application scope, I think it is at least as impressive as GPT-3, if not more so.
We are living in exciting times. And I think it’s important to understand that we are seeing the beginning of something, not the end. What will be possible in 20 years? Many things will happen, not all of them good. I’m not much of an expert in AI risks and besides, discussing them here in detail would go far beyond the scope of this blog post. Still I’m asking myself, how we as a society will cope with these – at the moment still largely unknown – changes.
My role at Creative Virtual involves looking at all the new technologies that pop up and evaluate if and how we can use them. So I have a bit of a front row seat in watching this unfold. I encourage you to check out our ChatGPT, GPT-3, and Your Conversational AI Solution blog post for a closer look at how we see these recent developments fitting with the work Creative Virtual does in the customer service and employee support space.
I think it is of the utmost importance that as many people as possible have a basic understanding about what’s going on. An ignorant society will not be able to react. I am trying to play my small part by sharing my knowledge with as many people as possible. Just recently, when an old friend of mine called, my wife burst out: ‘Great, now you can talk his ears off!’