The AGI Company Present AGENT Q The AI Master of the Impossible
The AGI Company has intorduced Agent Q.
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00:00AI has come a long way with models like ChatGPT and Llama3 that can handle language tasks
00:08like writing and coding pretty well.
00:10But when it comes to making decisions in complex multi-step situations, like organizing an
00:15international trip, coordinating flights, hotels, car rentals, and activities across
00:19different countries, if it misses a flight connection or books the wrong hotel, the entire
00:23trip could be thrown off course.
00:26Until now.
00:27That's when Agent Q comes into play.
00:29The team at the AGI company, working with folks at Stanford University, set out to tackle
00:34this exact problem.
00:35They wanted to create an AI that's not only good at understanding language, but also capable
00:41of making smart decisions in these kinds of complex multi-step tasks.
00:45What they came up with is pretty impressive.
00:47Let's break down how Agent Q works and why it's so different from other AI systems out
00:52there.
00:53Traditionally, AI models are trained on static datasets.
00:56They learn from a massive amount of data.
00:58And once they've seen enough examples, they can perform certain tasks reasonably well.
01:03But the problem is, this approach doesn't work as well when the AI is faced with tasks
01:08that require making decisions over several steps, especially in unpredictable environments
01:13like the web.
01:14For instance, booking a reservation on a real website where the layout and available options
01:19might change depending on the time of day or location can trip up even advanced models.
01:24So how does Agent Q solve this?
01:26The researchers combined a couple of advanced techniques to give the AI a much better chance
01:30at success.
01:31First, they used something called Monte Carlo Tree Search, or MCTS for short.
01:36MCTS is a method that helps the AI explore different possible actions and figure out
01:40which ones are likely to lead to the best outcome.
01:43It's been used successfully in game-playing AIs, like those that dominate in chess and
01:48Go, where exploring different strategies is key.
01:51But MCTS alone isn't enough because in real-world tasks, you don't always get clear feedback
01:56after every action.
01:57That's where the second technique comes in, Direct Preference Optimization, or DPO.
02:02This method allows the AI to learn from both its successes and its failures, gradually
02:06improving its decision-making over time.
02:09The AI doesn't just rely on a simple win or lose outcome.
02:12Instead, it analyzes the entire process, identifying which decisions were good and which ones weren't,
02:18even if the final result was a success.
02:21This combination of exploration with MCTS and reflective learning with DPO is what makes
02:26AgentQ stand out.
02:27To test this new approach, the researchers put AgentQ to work in a simulated environment
02:32called WebShop.
02:33This is essentially a fake online store where the AI has to complete tasks like finding
02:38specific products.
02:39It's a controlled environment, but it's designed to mimic the complexities of real e-commerce
02:44sites.
02:45And the results?
02:46AgentQ outperformed other AI models by a significant margin.
02:50While typical models that relied on simple supervised learning or even reinforcement
02:54learning had a success rate hovering around 28.6%, AgentQ, with its advanced reasoning
03:00and learning capabilities, boosted that rate to an impressive 50.5%.
03:05That's nearly double the performance, which is a huge deal in AI terms.
03:10But the real test came when the researchers took AgentQ out of the lab and into the real
03:15world.
03:16They tried it on an actual task, booking a table on OpenTable, a popular restaurant reservation
03:21website.
03:22Now, if you've ever used OpenTable, you know it's not always straightforward.
03:27Depending on the time, location, and restaurant, the options you see can vary.
03:31The AI had to navigate all of this and make a successful reservation.
03:36Before AgentQ got involved, the best AI model they had, Llama370B, had a success rate of
03:42just 18.6% on this task.
03:44Think about that.
03:45Only about one in five attempts actually resulted in a successful reservation.
03:49But after just one day of training with AgentQ, that success rate shot up to 81.7%.
03:56And it didn't stop there.
03:58When they equipped AgentQ with the ability to perform online searches to gather more
04:02information, the success rate climbed even higher to an incredible 95.4%.
04:09That's on par with, if not better than, what a human could do in the same situation.
04:13The leap in performance comes from the way AgentQ learns and improves over time.
04:19Traditional AI models are like straight-A students.
04:21They excel in familiar scenarios, but can struggle when faced with the unexpected.
04:26In contrast, AgentQ acts more like an experienced problem solver capable of adapting to new
04:31situations.
04:32By integrating MCTS with DPO, AgentQ moves beyond simply following predefined rules,
04:38instead learning from each experience and improving with every attempt.
04:42One of the challenges the researchers faced was ensuring that the AI could make these
04:47improvements without causing too many problems along the way.
04:50When you're dealing with real-world tasks, especially those involving sensitive actions
04:54like online bookings or payments, you need to be careful.
04:58An AI that makes a mistake could end up reserving the wrong date, or worse, sending money to
05:02the wrong account.
05:03To handle this, the team built in mechanisms that allow the AI to backtrack and correct
05:08its actions if things go wrong.
05:10They also used something called a replay buffer, which helps the AI remember past actions and
05:15learn from them without having to repeat the same mistakes over and over.
05:19Another interesting aspect of AgentQ is its ability to use what the researchers call self-critique.
05:25After taking an action, the AI doesn't just move on to the next step.
05:28It stops and evaluates what it just did.
05:31This self-reflection is guided by an AI-based feedback model that ranks possible actions
05:37and suggests which ones are likely to be the best.
05:40This process helps the AI fine-tune its decision-making in real-time, making it more reliable and
05:45effective at completing tasks.
05:48We mentioned earlier that the LLAMA370B model had a starting success rate of 18.6% when
05:54trying to book a reservation on OpenTable.
05:57After using AgentQ's framework for just a day, that jumped to 81.7%, and with online
06:02search capability, it hit 95.4%.
06:06To put that into perspective, that's a 340% relative increase in success rate from the
06:12original performance.
06:14And when you consider that the average human success rate on the same task is around 50%,
06:19it's clear that AgentQ isn't just catching up to human-level performance, it's surpassing it.
06:24What's also fascinating is how AgentQ handles the complexity of real-world environments
06:28compared to simpler, simulated ones like WebShop.
06:31In WebShop, the tasks were relatively straightforward, and the AI could complete them in an
06:36average of about 6.8 steps.
06:38But when it came to the OpenTable environment, the tasks were much more complex, requiring
06:44an average of 13.9 steps to complete.
06:47Despite this added complexity, AgentQ was able to not only handle the tasks, but also
06:52excel at them.
06:53This shows that the AI's ability to learn and adapt isn't just a fluke, it's robust
06:57enough to deal with the kind of unpredictability you'd find in the real world.
07:02But this isn't to say everything is perfect.
07:04The researchers are aware that there are still some challenges to overcome.
07:08For one, while AgentQ's self-improvement capabilities are impressive, there's always
07:12a risk when you let an AI operate autonomously in sensitive environments.
07:17The team is working on ways to mitigate these risks, possibly by incorporating more human
07:21oversight or additional safety checks.
07:24They're also exploring different search algorithms to see if there's an even better way for
07:28the AI to explore and learn from its environment.
07:31While MCTs has been incredibly successful, especially in games and reasoning tasks, there
07:35might be other approaches that could push the performance even further.
07:39One of the most interesting points the researchers raise is the gap between the AI's zero-shot
07:44performance and its performance when equipped with search capabilities.
07:49Zero-shot means the AI is trying to solve a problem it hasn't seen before, and typically
07:53this is really challenging.
07:54Even advanced models can struggle here.
07:56But what's fascinating about AgentQ is that once you give it the ability to search and
08:00explore, its performance skyrockets.
08:03This suggests that the key to making AI more reliable in real-world tasks isn't just
08:08about training it on more data, it's about giving it the tools to actively explore and
08:12learn from its environment in real time.
08:15So essentially, we're looking at AI systems that can handle increasingly complex tasks
08:20with minimal supervision, which opens up a lot of possibilities.
08:24Whether it's managing your bookings, navigating through complicated online systems, or even
08:29tackling more advanced tasks like legal document analysis, the potential applications are vast,
08:35and as these systems continue to improve, we might find ourselves relying on them more
08:40and more for tasks that currently require a lot of manual effort.
08:45Alright, if you found this interesting, make sure to hit that like button, subscribe, and
08:49stay tuned for more AI insights.
08:51Thanks for watching, and I'll catch you in the next one.