Yes, humans still provide value. Correspondence chess players will for example read chess opening books to find if there any mistakes in that book and even if they find just one, they’ll try to lead their opponent into that dubious line, which is often a mistake that computers can’t easily spot. Also as a former highly-ranked chess player, I’d use multiple chess engines at the same time to compare and contrast and also I’d know their strengths and weaknesses and which possibilities to explore.
Time should also be a factor when comparing strength between AI alone and an AI-human team. Humans might add to correspondence chess but it will cost them a significant amount of time. Human-AI teams are very slow compared to AI alone.
For example in low latency algorithmic stock trading reaction times are below 10ms. Human reaction time is 250ms. A human-AI cooperation of stock traders would have a minimum reaction time of 250ms (if the human immediatly agrees when the AI suggests a trade), This is way to slow and means a serious competitive disadvantage.
Take this to strategically aware AI compared to a human working with a strategically aware AI. And suppose that the human can improve the strategic decision if given enough time. The AI alone would be at least a 100x faster that the AI-human team. A serious advantage for the AI alone.
For the more mundane human in the loop applications speed and cost will probably be a deciding factor. If chess was a job than most of the time a Magnus Carlson level move in a few seconds for a few cents will be sufficient. In rare cases (e.g. cutting edge science) it might be valuable to go for the absolute best decision at a higher cost in time and money.
So my guess is that human in the loop solutions will be a short fase in the coming transition. The human in the loop fase will provide valuable data for the AI, but soon monetary and time costs will move processes towards an AI alone setup instead of humans in te loop.
Even if in correspondence chess AI-human teams are better it probably does not transfer to a lot of real world applications.
I think you are underrating the number of high-stakes decisions in the world. A few examples: whether or not to hire someone, the design of some mass-produced item, which job to take, who to marry. There are many more.
These are all cases where making the decision 100x faster is of little value, because it will take a long time to see if the decision was good or not after it is made. And where making a better decision is of high value. (Many of these will also be the hardest tasks for AI to do well on, because there is very little training data about them).
True, it depends on the ratio mundane and high stakes decisions. Athough there are high stakes decisions that are also time dependant. See the example about high frequency trading (no human in the loop and the algorithm makes trades in the millions).
Furthermore your conclusion that time independant high stakes decisions will be the tasks where humans provide most value seems true to me. AI will easily be superior when there are time constraint. Absent such constraints, humans will have a better chance of competing with AI. And economic strategic decisions are often times not extremely time constrained (at least a couple of hours or days of time).
In economic situations the amount of high stakes decisions will be limited (only a few people make desicions about large sums of money and strategy) . Given a multinational with a 100.000 employees, only very few will take high stake decisions. But these decisions might have a significant impact on competitiveness. Thus the multinational with a human ceo might out compete a full AI company.
In a strategic situation time might give more of an advantage (i am economist not a military expert so I am really guessing here). My guess would be that a drone without a human in the loop could have a significant advantage (thus pressures might rise to push for high stake decision making by drones (human lives)).
Yes, humans still provide value. Correspondence chess players will for example read chess opening books to find if there any mistakes in that book and even if they find just one, they’ll try to lead their opponent into that dubious line, which is often a mistake that computers can’t easily spot. Also as a former highly-ranked chess player, I’d use multiple chess engines at the same time to compare and contrast and also I’d know their strengths and weaknesses and which possibilities to explore.
Time should also be a factor when comparing strength between AI alone and an AI-human team. Humans might add to correspondence chess but it will cost them a significant amount of time. Human-AI teams are very slow compared to AI alone.
For example in low latency algorithmic stock trading reaction times are below 10ms. Human reaction time is 250ms. A human-AI cooperation of stock traders would have a minimum reaction time of 250ms (if the human immediatly agrees when the AI suggests a trade), This is way to slow and means a serious competitive disadvantage.
Take this to strategically aware AI compared to a human working with a strategically aware AI. And suppose that the human can improve the strategic decision if given enough time. The AI alone would be at least a 100x faster that the AI-human team. A serious advantage for the AI alone.
For the more mundane human in the loop applications speed and cost will probably be a deciding factor. If chess was a job than most of the time a Magnus Carlson level move in a few seconds for a few cents will be sufficient. In rare cases (e.g. cutting edge science) it might be valuable to go for the absolute best decision at a higher cost in time and money.
So my guess is that human in the loop solutions will be a short fase in the coming transition. The human in the loop fase will provide valuable data for the AI, but soon monetary and time costs will move processes towards an AI alone setup instead of humans in te loop.
Even if in correspondence chess AI-human teams are better it probably does not transfer to a lot of real world applications.
I think you are underrating the number of high-stakes decisions in the world. A few examples: whether or not to hire someone, the design of some mass-produced item, which job to take, who to marry. There are many more.
These are all cases where making the decision 100x faster is of little value, because it will take a long time to see if the decision was good or not after it is made. And where making a better decision is of high value. (Many of these will also be the hardest tasks for AI to do well on, because there is very little training data about them).
True, it depends on the ratio mundane and high stakes decisions. Athough there are high stakes decisions that are also time dependant. See the example about high frequency trading (no human in the loop and the algorithm makes trades in the millions).
Furthermore your conclusion that time independant high stakes decisions will be the tasks where humans provide most value seems true to me. AI will easily be superior when there are time constraint. Absent such constraints, humans will have a better chance of competing with AI. And economic strategic decisions are often times not extremely time constrained (at least a couple of hours or days of time).
In economic situations the amount of high stakes decisions will be limited (only a few people make desicions about large sums of money and strategy) . Given a multinational with a 100.000 employees, only very few will take high stake decisions. But these decisions might have a significant impact on competitiveness. Thus the multinational with a human ceo might out compete a full AI company.
In a strategic situation time might give more of an advantage (i am economist not a military expert so I am really guessing here). My guess would be that a drone without a human in the loop could have a significant advantage (thus pressures might rise to push for high stake decision making by drones (human lives)).