what is a problem solving agent in ai

Problem-Solving Agents In Artificial Intelligence

In artificial intelligence, a problem-solving agent refers to a type of intelligent agent designed to address and solve complex problems or tasks in its environment. These agents are a fundamental concept in AI and are used in various applications, from game-playing algorithms to robotics and decision-making systems. Here are some key characteristics and components of a problem-solving agent:

• Perception : Problem-solving agents typically have the ability to perceive or sense their environment. They can gather information about the current state of the world, often through sensors, cameras, or other data sources.
• Knowledge Base : These agents often possess some form of knowledge or representation of the problem domain. This knowledge can be encoded in various ways, such as rules, facts, or models, depending on the specific problem.
• Reasoning : Problem-solving agents employ reasoning mechanisms to make decisions and select actions based on their perception and knowledge. This involves processing information, making inferences, and selecting the best course of action.
• Planning : For many complex problems, problem-solving agents engage in planning. They consider different sequences of actions to achieve their goals and decide on the most suitable action plan.
• Actuation : After determining the best course of action, problem-solving agents take actions to interact with their environment. This can involve physical actions in the case of robotics or making decisions in more abstract problem-solving domains.
• Feedback : Problem-solving agents often receive feedback from their environment, which they use to adjust their actions and refine their problem-solving strategies. This feedback loop helps them adapt to changing conditions and improve their performance.
• Learning : Some problem-solving agents incorporate machine learning techniques to improve their performance over time. They can learn from experience, adapt their strategies, and become more efficient at solving similar problems in the future.

Problem-solving agents can vary greatly in complexity, from simple algorithms that solve straightforward puzzles to highly sophisticated AI systems that tackle complex, real-world problems. The design and implementation of problem-solving agents depend on the specific problem domain and the goals of the AI application.

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

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Problem Solving Agents in Artificial Intelligence

In this post, we will talk about Problem Solving agents in Artificial Intelligence, which are sort of goal-based agents. Because the straight mapping from states to actions of a basic reflex agent is too vast to retain for a complex environment, we utilize goal-based agents that may consider future actions and the desirability of outcomes.

You Will Learn

Problem Solving Agents

Problem Solving Agents decide what to do by finding a sequence of actions that leads to a desirable state or solution.

An agent may need to plan when the best course of action is not immediately visible. They may need to think through a series of moves that will lead them to their goal state. Such an agent is known as a problem solving agent , and the computation it does is known as a search .

The problem solving agent follows this four phase problem solving process:

• Goal Formulation: This is the first and most basic phase in problem solving. It arranges specific steps to establish a target/goal that demands some activity to reach it. AI agents are now used to formulate goals.
• Problem Formulation: It is one of the fundamental steps in problem-solving that determines what action should be taken to reach the goal.
• Search: After the Goal and Problem Formulation, the agent simulates sequences of actions and has to look for a sequence of actions that reaches the goal. This process is called search, and the sequence is called a solution . The agent might have to simulate multiple sequences that do not reach the goal, but eventually, it will find a solution, or it will find that no solution is possible. A search algorithm takes a problem as input and outputs a sequence of actions.
• Execution: After the search phase, the agent can now execute the actions that are recommended by the search algorithm, one at a time. This final stage is known as the execution phase.

Problems and Solution

Before we move into the problem formulation phase, we must first define a problem in terms of problem solving agents.

A formal definition of a problem consists of five components:

Initial State

Transition model.

It is the agent’s starting state or initial step towards its goal. For example, if a taxi agent needs to travel to a location(B), but the taxi is already at location(A), the problem’s initial state would be the location (A).

It is a description of the possible actions that the agent can take. Given a state s, Actions ( s ) returns the actions that can be executed in s. Each of these actions is said to be appropriate in s.

It describes what each action does. It is specified by a function Result ( s, a ) that returns the state that results from doing action an in state s.

The initial state, actions, and transition model together define the state space of a problem, a set of all states reachable from the initial state by any sequence of actions. The state space forms a graph in which the nodes are states, and the links between the nodes are actions.

It determines if the given state is a goal state. Sometimes there is an explicit list of potential goal states, and the test merely verifies whether the provided state is one of them. The goal is sometimes expressed via an abstract attribute rather than an explicitly enumerated set of conditions.

It assigns a numerical cost to each path that leads to the goal. The problem solving agents choose a cost function that matches its performance measure. Remember that the optimal solution has the lowest path cost of all the solutions .

Example Problems

The problem solving approach has been used in a wide range of work contexts. There are two kinds of problem approaches

• Standardized/ Toy Problem: Its purpose is to demonstrate or practice various problem solving techniques. It can be described concisely and precisely, making it appropriate as a benchmark for academics to compare the performance of algorithms.
• Real-world Problems: It is real-world problems that need solutions. It does not rely on descriptions, unlike a toy problem, yet we can have a basic description of the issue.

Some Standardized/Toy Problems

Vacuum world problem.

Let us take a vacuum cleaner agent and it can move left or right and its jump is to suck up the dirt from the floor.

The vacuum world’s problem can be stated as follows:

States: A world state specifies which objects are housed in which cells. The objects in the vacuum world are the agent and any dirt. The agent can be in either of the two cells in the simple two-cell version, and each call can include dirt or not, therefore there are 2×2×2 = 8 states. A vacuum environment with n cells has n×2 n states in general.

Initial State: Any state can be specified as the starting point.

Actions: We defined three actions in the two-cell world: sucking, moving left, and moving right. More movement activities are required in a two-dimensional multi-cell world.

Transition Model: Suck cleans the agent’s cell of any filth; Forward moves the agent one cell forward in the direction it is facing unless it meets a wall, in which case the action has no effect. Backward moves the agent in the opposite direction, whilst TurnRight and TurnLeft rotate it by 90°.

Goal States: The states in which every cell is clean.

Action Cost: Each action costs 1.

8 Puzzle Problem

In a sliding-tile puzzle , a number of tiles (sometimes called blocks or pieces) are arranged in a grid with one or more blank spaces so that some of the tiles can slide into the blank space. One variant is the Rush Hour puzzle, in which cars and trucks slide around a 6 x 6 grid in an attempt to free a car from the traffic jam. Perhaps the best-known variant is the 8- puzzle (see Figure below ), which consists of a 3 x 3 grid with eight numbered tiles and one blank space, and the 15-puzzle on a 4 x 4  grid. The object is to reach a specified goal state, such as the one shown on the right of the figure. The standard formulation of the 8 puzzles is as follows:

STATES : A state description specifies the location of each of the tiles.

INITIAL STATE : Any state can be designated as the initial state. (Note that a parity property partitions the state space—any given goal can be reached from exactly half of the possible initial states.)

ACTIONS : While in the physical world it is a tile that slides, the simplest way of describing action is to think of the blank space moving Left , Right , Up , or Down . If the blank is at an edge or corner then not all actions will be applicable.

TRANSITION MODEL : Maps a state and action to a resulting state; for example, if we apply Left to the start state in the Figure below, the resulting state has the 5 and the blank switched.

GOAL STATE :  It identifies whether we have reached the correct goal state. Although any state could be the goal, we typically specify a state with the numbers in order, as in the Figure above.

ACTION COST : Each action costs 1.

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Unlocking the Power of Problem-Solving Agents in AI

Updated on Feb 19,2024

Table of Contents

Introduction, what is problem-solving agent, understanding the role of support in problem-solving, starting the problem-solving process, the efficiency of golf state in problem-solving, benefits of subscribing to problem-solving agents, incorporating quiz function in problem-solving, the resultant state and its significance, the use of problem-solving agent in medicine.

In today's world, problem-solving skills are highly valued, especially in exams where the likelihood of encountering questions related to this topic is significantly high. It is important to delve deeper into understanding what exactly a problem-solving agent entails. Essentially, a problem-solving agent refers to the support system that helps in identifying and resolving problems. Whether it is through chat or golf, the aim is to find the starting point to solve the problem efficiently. By subscribing to problem-solving agents, users gain access to their expertise and follow a systematic approach towards resolving issues. This article will explore the concept of problem-solving agents in detail, highlighting their benefits and how they can be effectively utilized in various domains.

A problem-solving agent serves as a crucial tool in addressing and resolving various issues. It acts as a support system that systematically identifies and solves problems. The initial step in utilizing a problem-solving agent is to start a chat, wherein the user describes the problem that needs to be solved. The agent then navigates through the problem-solving process, searching for the best Course of action to address the problem efficiently. This may involve accessing Relevant information, utilizing the golf state, or following a predefined set of steps. Ultimately, the problem-solving agent aims to provide solutions and guide users towards resolving their problems effectively.

Support plays a vital role in the problem-solving process. When a problem is presented to a problem-solving agent, it takes the responsibility of finding the most suitable approach to solve it. The agent starts by analyzing the problem, identifying its root causes, and devising an action plan accordingly. The support provided by the problem-solving agent is crucial as it guides users in determining Where To begin the problem-solving process. Whether it is through chat or accessing relevant resources, the support system ensures that users have a clear direction to follow in order to reach a solution.

The starting point of the problem-solving process is crucial in determining the efficiency and effectiveness of the overall solution. By initiating a chat, users can provide the necessary information about the problem at HAND . This allows the problem-solving agent to understand the context and formulate the most appropriate course of action. The initial chat sets the foundation for resolving the problem, ensuring that the subsequent steps are focused on finding the optimal solution. It serves as a starting point to Gather relevant insights and create a roadmap that leads towards problem resolution.

Golf state is an efficient approach in problem-solving that aids in finding the best solution. By exploring various options and evaluating their outcomes, the agent can navigate through different states to reach the most desirable outcome. The golf state allows the problem-solving agent to assess the potential outcomes of different actions and select the one that yields the desired results. This method of problem-solving guarantees efficiency and effectiveness as it considers the consequences of each step before proceeding further.

Subscribing to problem-solving agents offers numerous benefits in addressing various issues. Firstly, it provides access to expert support, ensuring that users receive guidance from professionals in the respective field. Moreover, problem-solving agents streamline the problem-solving process and enable users to follow a systematic approach towards finding a solution. Additionally, subscribing to these agents enhances efficiency by providing quick and accurate responses to problems. The utilization of problem-solving agents saves time and effort while delivering effective solutions.

Integrating a quiz function in problem-solving enhances the overall learning experience. By incorporating Quizzes , problem-solving agents can assess the user's understanding of the problem and recommend appropriate solutions. Quizzes serve as a knowledge check and help users gauge their expertise in problem-solving. Furthermore, they enable users to practice their problem-solving skills and enhance their competency in addressing various issues.

The resultant state in problem-solving signifies the successful resolution of the problem. It represents the endpoint where the problem-solving process has reached fruition, and a solution has been obtained. The resultant state is a crucial indicator of success and serves as a milestone in the problem-solving journey. Achieving the resultant state validates the effectiveness of the problem-solving agent and demonstrates the user's ability to overcome challenges.

Problem-solving agents have found application in various fields, including medicine. In the medical domain, problem-solving agents aid in diagnosing and resolving medical issues. They analyze symptoms, medical history, and test results to arrive at accurate diagnoses and suggest appropriate treatment plans. The utilization of problem-solving agents in medicine enhances efficiency and ensures accurate and Timely medical interventions.

Problem-solving agents play a significant role in addressing and resolving various issues efficiently. By subscribing to these agents, users gain access to expert support, which guides them through the problem-solving process. The integration of quizzes and the utilization of golf states further enhance the problem-solving experience. Whether in medicine or other domains, problem-solving agents offer valuable assistance in finding optimal solutions. By employing these agents, users can overcome challenges and achieve successful outcomes.

• Problem-solving agents act as support systems to address and resolve problems efficiently.
• The starting point in problem-solving is crucial in determining the effectiveness of the solution.
• Golf state is an efficient approach that evaluates potential outcomes to find the best solution.
• Subscribing to problem-solving agents provides expert guidance and streamlines the problem-solving process.
• Quizzes enhance the learning experience and help users improve their problem-solving skills.
• Problem-solving agents are utilized in various fields, including medicine, for accurate diagnoses and treatment plans.

The above is a brief introduction to Unlocking the Power of Problem-Solving Agents in AI

Let's move on to the first section of Unlocking the Power of Problem-Solving Agents in AI

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Ravi Sawhney

May 24th, 2024, how to enhance generative ai’s problem-solving capabilities and boost workplace productivity.

0 comments | 8 shares

Estimated reading time: 5 minutes

In 2024, enterprise software companies are betting on generative AI, in a quest to enhance productivity. OpenAI has recently released GPT-4o, which includes interpretation and generation of voice and vision. Ravi Sawhney discusses how to incorporate this technology into end-user workplace technology and introduces the concept of multi-agent workflows, an idea that allows organisations to imitate entire knowledge teams.

Back in 2021 I wrote a piece for LSE Business Review in which I demonstrated the power of OpenAI’s GPT3 in being able to interpret human language by converting it into code. At the time, the technology was in its infancy and didn’t generate the spark ChatGPT did when it was released to the public in November 2022, that moment truly ignited the generative AI (GenAI) boom. Here I provide some personal thoughts on why GenAI matters and the challenges in using it for work. I also introduce the concept of multi-agent workflows as a method to boost the potential of where we can go.

It’s all about productivity

In 2024, nearly all enterprise software companies are making bets on GenAI, which perhaps has taken away some of the limelight from existing machine learning approaches such as supervised and unsupervised learning that are still crucial parts of any complete AI framework. The premise of why organisations are doing this all ties back to what got me interested in this technology in the first place: productivity.

In its more basic form, GenAI can be thought of as the most powerful autocomplete technology we have ever seen. The ability of large language models (LLMs) to predict the next word is so good that it can step in and perform knowledge worker tasks such as classifying, editing, summarising, questions and answers as well as content creation.

Additionally, variations of this technology can operate across modalities, much like human senses, to include interpretation and generation across voice and vision. In fact, in 2024 the nomenclature is shifting from LLMs to large multimodal models (LMMs) and the recent release of GPT-4o from OpenAI is evidence of this. Whether the step-in process is advisory, with a human in-the-loop or full-blown automated decision-making, it is not hard to see how GenAI has the potential to deliver transformational boost to labour productivity across the knowledge working sector. A recent paper  on this very topic estimated that, when used to drive task automation, GenAI could boost labour productivity by 3.3 percentage points annually, creating $4.4 trillion of value to global GDP.

The productivity benefits perhaps take us closer to the aspiration Keynes had when he wrote Economic Possibilities for our Grandchildren in 1930, in which he forecast that in a hundred years, thanks to technological advancements improving the standard of living, we could all be doing 15-hour work weeks. This sentiment was echoed by Nobel Prize winner in economics Sir Chirstopher Pissarides, who said ChatGPT could herald 4-day work week.

So, if the potential to meaningfully transform how we work is right in front of us and is being developed at breakneck speed, then how do we bridge the gap to make this possibility a reality?

Trust and tooling

Two typical challenges need to be considered when incorporating this technology into end-user workplace technology. The largest, arguably, is managing the trust issue. By default, LLMs do not have access to your own private information, so asking it about a very specific support issue on your own product will typically produce a confident but inaccurate response, typically referred to as “hallucinations”. Fine-tuning LLMs on your own data is one option, albeit an expensive one given the hardware requirements. A much more approachable method that has become commonplace in the community is referred to as retrieval-augmented-generation (RAG) . This is where your private data is brought into the query prompt using the power of embeddings to perform lookups from a given query. This resultant response is synthesised from this data along with the LLM’s existing knowledge, resulting in something that could be considered useful, albeit with some appropriate user guidance.

The second challenge is maths. While LLMs, with some careful prompting, could create a unique, compelling and (importantly) convincing story from scratch, it would struggle with basic to intermediate maths, depending on the foundation model you are using. Here the community has introduced the concept of tooling or sometimes referred to as agents. In this paradigm, the LLM can categorise the query being asked and, rather than trying to answer it, call the appropriate ‘tool’ for the job . For example, if being asked about the weather outside, it might call a weather API service. If being asked to perform math, it would route the query to a calculator API. And if it needs to retrieve information from a database, it might convert the request to SQL or Pandas, execute the resulting code in a sandbox environment and return the result to the user, who might be none the wiser about what is going on under the hood.

The potential of multi-agent workflows

Agent frameworks with tooling are expanding the possibilities of how LLMs can be used to solve real-world problems today. However, they still largely fall short of being able to perform complex knowledge-work tasks due to limitations such as lack of memory, planning and reasoning capabilities. Multi-agent frameworks present an opportunity to tackle some of these challenges. A good way to understand how they could work is to draw a contrast with System 1 and System 2 thinking , popularised by Daniel Kahneman.

Think of System 1 as your gut instinct: fast, automatic and intuitive. In the world of LLMs, that’s like the model’s ability to generate human-like responses based on its vast training data. In contrast, System 2 thinking is slower, more deliberate, and logical, representing the model’s capacity for structured, step-by-step problem-solving and reasoning.

To fully unleash the potential of LLMs, we need to develop techniques that leverage both System 1 and System 2 capabilities. By breaking down complex tasks into smaller, manageable steps, we can guide LLMs to perform more structured and reliable problem-solving, akin to how humans would solve challenges.

Consider a team of agents, each assigned a specific role through prompt engineering, working together to tackle a single goal. That’s essentially what agent workflows, or sometimes referred to as agentic workflows, do for LLMs. Each agent is responsible for a specific subtask, and they communicate with each other, passing information and results back and forth until the overall task is complete. By designing prompts that encourage logical reasoning, step-by-step problem-solving, and collaboration with other agents, we can create a system that mimics the deliberate and rational thinking associated with System 2.

Here is where this gets exciting: agent workflows could allow us to imitate entire knowledge teams. Imagine a virtual team of AI agents, each with its workflow’s own specialism, collaborating to solve problems and make decisions just like a human team would. This could revolutionise the way we work, allowing us to tackle more complex challenges with zero or minimal human-in-the-loop supervision. It also opens the idea of allowing us to simulate how teams will react to events in a sandbox environment, where every team member is modelled as an agent in the workflow. The conversational outputs could even be saved for retrieval latter, serving as long-term memory.

By combining the raw power of System 1 thinking with the structured reasoning of System 2, we can create AI systems that not only generate human-like responses but can also tackle more complex tasks and move towards solving problems. The future of work is here, and it’s powered by the symbiosis of human ingenuity and artificial intelligence.

• Authors’ disclaimer: All views expressed are my own.
• This blog post represents the views of the author(s), not the position of LSE Business Review or the London School of Economics and Political Science.
• Featured image  provided by Shutterstock
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About the author

Ravi Sawhney is Head of Buyside Execution at Bloomberg LP. He has wide experience in the financial markets. Ravi holds a Bsc Hons in economics from LSE.

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Title: llm-based multi-agent reinforcement learning: current and future directions.

Abstract: In recent years, Large Language Models (LLMs) have shown great abilities in various tasks, including question answering, arithmetic problem solving, and poem writing, among others. Although research on LLM-as-an-agent has shown that LLM can be applied to Reinforcement Learning (RL) and achieve decent results, the extension of LLM-based RL to Multi-Agent System (MAS) is not trivial, as many aspects, such as coordination and communication between agents, are not considered in the RL frameworks of a single agent. To inspire more research on LLM-based MARL, in this letter, we survey the existing LLM-based single-agent and multi-agent RL frameworks and provide potential research directions for future research. In particular, we focus on the cooperative tasks of multiple agents with a common goal and communication among them. We also consider human-in/on-the-loop scenarios enabled by the language component in the framework.

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Characteristics of Artificial Intelligence Problems

Problems in Artificial Intelligence (AI) come in different forms, each with its own set of challenges and potential for innovation. From image recognition to natural language processing, AI problems exhibit distinct characteristics that shape the strategies and techniques used to tackle them effectively. In this article, we delve into the fundamental characteristics of AI problems, providing light on what makes them so fascinating and formidable.

Table of Content

Key Terminologies in Artificial Intelligence Problems

Addressing the challenges of ai problems, examples of ai applications and challenges across domains, characteristics of artificial intelligence problems – faqs.

Before exploring the characteristics, let’s clarify some essential AI concepts:

• Problem-solving: Problem-solving is a process that is a solution provided to a complex problem or task. When dealing with AI, problem-solving involves creating algorithms and methods of artificial intelligence that will empower machines to imitate humans’ capabilities of logical and reasonable thinking in certain situations.
• Search Space: Searching space refers to the area where an agent involved in the problem-solving process can examine all the possible states or settings with the hope of discovering a solution. It covers a gamut of options that the agent might select for arriving at the same destination.
• State: An entity represents some unique and specific arrangement of elements in a problem-solving situation. States can be assigned to different locations, challenges, or dangers that the problem-solving agent faces while looking for a solution to the problem within the search space.
• Search Algorithm: A search algorithm describes any process or method targeted for examining and exploring the given problem space to find a solution. Algorithm decision-making has diverging levels of complexity and effectiveness. They are studied to help in the discovery of the most suitable results.
• Heuristic: Heuristic is a thumb rule or guiding principle that is used to make intelligent decisions or solve the problems that are encountered during the process. Applying heuristics in AI is prevalent in prioritizing search paths or evaluating probable solutions based on their likelihood of finishing successfully.
• Optimization: The problem of optimization implies finding the best solution for process selection among the set of feasible alternatives submitted to some previously set objectives or criteria. AI optimization approaches are employed to deal optimally with complex issues through performance and efficiency improvement.

By understanding these key terminologies, we can better grasp the characteristics of AI problems and the techniques used to address them. These concepts form the foundation of AI problem-solving and provide the framework for developing innovative solutions to real-world challenges.

Let’s explore the core characteristics that differentiate AI problems:

• Learning and adaptation: AI systems should be capable of learning from data or experiences and adapting their behaviour accordingly. This enables them to improve performance over time and handle new situations more effectively.
• Complexity: AI problems often involve dealing with complex systems or large amounts of data. AI systems must be able to handle this complexity efficiently to produce meaningful results.
• Uncertainty: AI systems frequently operate in environments where outcomes are uncertain or incomplete information is available. They must be equipped to make decisions or predictions under such conditions.
• Dynamism: Environments in which AI systems operate can change over time. These changes may occur unpredictably or according to specific rules, requiring AI systems to continually adjust their strategies or models.
• Interactivity : Many AI applications involve interaction with users or other agents. Effective AI systems should be able to perceive, interpret, and respond to these interactions in a meaningful way.
• Context dependence: The behavior or performance of AI systems may depend on the context in which they operate. Understanding and appropriately responding to different contexts is essential for achieving desired outcomes.
• Multi-disciplinary: AI problems often require knowledge and techniques from multiple disciplines, including computer science, mathematics, statistics, psychology, and more. Integrating insights from these diverse fields is necessary for developing effective AI solutions.
• Goal-oriented Design: AI systems are typically designed to achieve specific objectives or goals. Designing AI systems with clear objectives in mind helps guide the development process and ensures that the resulting systems are focused on achieving meaningful outcomes.

These characteristics collectively shape the challenges and opportunities involved in developing and deploying AI systems across various domains and applications.

The characteristics of AI problems present unique challenges that require innovative approaches to solution development. Some of the key aspects to consider in tackling these challenges include:

• Complexity and Uncertainty: AI difficulties are sometimes characterized by highly variable domains that are difficult to predict exactly. Hence, AI algorithms should be installed with the skill of dealing with unclear circumstances and should make decisions that are based on imperfect data or noisy information.
• Algorithmic Efficiency: Among the key challenges of this approach are the enormous search spaces, computational resources, and the efficiency of the algorithms in terms of problem-solving. Strategies like caching, pruning, and parallelization are among the most widely used implementations for better algorithmic speed.
• Domain Knowledge Integration: Such numerous AI problems involve the ability to capture the rules and reasoning of the real world to model and solve the questions correctly. The AI machines that have been trained with expertise from relevant domains improve the accuracy and effectiveness of the applications in the real world.
• Scalability and Adaptability: AI solutions should be able to process large datasets and complex cases at the same time, and they should also be versatile by responding to shifts in conditions and requirements. Strategies such as machine learning and reinforcement learning allow systems to do more than just perform according to the given tasks at hand; they empower systems to learn and progress over time.
• Ethical and Social Implications: AI technologies elicit ethical and social limitations concerning problems of bias, justice, privacy, and responsible office. Taking these implications into account, along with ethical frameworks, compliance frameworks, and stakeholder engagement, is essential. This approach will help position cryptocurrencies as a secure and trustworthy investment.
• Interpretability and Explainability: To achieve interpretability and explainability of AI algorithms for the sake of understanding and confidence among users and stakeholders, these algorithms should be knowable and comprehensible enough. Examples like chatbots producing natural-like conversation could better clarify the working scheme of AI technology.
• Robustness and Resilience: AI machinery should perform against its being hacked or affected by adversarial attacks, inaccuracies (errors), and environmental changes. Robustness testing, the construction of mechanisms for error handling, and the building up of redundancy must be taken seriously by AI systems to ensure their reliability and stability.
• Human-AI Collaboration: Successful human-AI entente is the key component to making the most of our advantages as well as artificial intelligence skills. Achieving AI solutions that are capable of supporting human skills and more importantly, preferences will reduce human efforts correspondingly and bring the best performance.

By addressing these challenges through innovative methodologies and interdisciplinary collaboration, we can harness the full potential of AI to solve complex problems and drive societal progress.

1. Robotics

Problem: A delivery robot navigating a busy warehouse to locate and retrieve a specific item.

Characteristics:

• Complexity: Industrial storage is networked, in the middle of things, with obstacles, and other robots and people moving unpredictably. This robot must process the visual scene, plan the route effectively, and detect and avoid possible collisions.
• Dynamism: A combination of outside factors leads to change, which is a constant inside the warehouse. Unpredictable system failures or spontaneous tasks can make the robot change its means and decision-making at the moment of need.
• Uncertainty: Sensor data (such as images obtained from a camera) might be noisy, incomplete, and unstable. The robot could be handling decisions based on fragmented or formless pieces of information.

2. Natural Language Processing (NLP)

Problem: A sentiment analysis system in NLP classifying customer reviews as positive, negative, or neutral.

• Subjectivity: Human language is nuanced. Sarcasm, irony, and figurative expressions can be difficult for machines to accurately interpret.
• Need for Context: Understanding sentiment may depend on cultural references, product-specific knowledge, or even the reviewer’s prior interactions with the company.
• Ambiguity: A single word or phrase could have multiple meanings, affecting the overall sentiment of the text.

3. Computer Vision

Problem: A medical image recognition system in Computer Vision designed to detect tumors in X-rays or MRI scans.

• Complexity: Medical images are highly detailed and can exhibit subtle variations. The system needs to distinguish between healthy tissue and potential abnormalities.
• Uncertainty: Images may contain noise or artifacts. The presence of a tumor might not be immediately obvious, requiring the system to handle ambiguity.
• Ethical Considerations: False positives or false negatives have serious consequences for patient health. Accuracy, transparency, and minimizing bias are crucial.

The premises of AI-based problems – complexity, uncertainty, subjectivity, and more, – bring an unavoidable difficulty to the table. These features must be known for building appropriate AI because this is necessary. Through the use of machine learning, probabilistic reasoning, and knowledge representation which are referred to as the tools in AI development alongside the ethical considerations, these designers and scientists can face such complexities well and give shape to AI in a way that will be beneficial to society.

Q. What are the core characteristics that differentiate AI problems?

The core characteristics of AI problems include complexity, uncertainty and ambiguity, lack of clear problem definition, non-linearity, dynamism, subjectivity, interactivity, context sensitivity, and ethical considerations.

Q. Can you explain the concept of problem-solving in AI?

Problem-solving in AI involves creating algorithms and methods that enable machines to imitate human capabilities of logical and reasonable thinking in certain situations.

Q. What is meant by the term “search space” in AI?

Search space refers to the area where an agent involved in the problem-solving process can examine all the possible states or settings with the hope of discovering a solution.

Q. How do AI algorithms address challenges such as complexity and uncertainty?

AI algorithms are designed to handle unclear circumstances and make decisions based on imperfect data or noisy information.

Q. What are some examples of AI applications and the challenges they face?

Examples include robotics (e.g., delivery robots navigating busy warehouses), natural language processing (e.g., sentiment analysis of customer reviews), and computer vision (e.g., medical image recognition for detecting tumors).

Q. What role do ethical considerations play in AI development?

Ethical considerations are crucial in AI development to address issues such as bias, justice, privacy, and responsibility, ensuring that AI technologies are deployed responsibly and ethically.

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The promise and the reality of gen AI agents in the enterprise

The evolution of generative AI (gen AI) has opened the door to great opportunities across organizations, particularly regarding gen AI agents—AI-powered software entities that plan and perform tasks or aid humans by delivering specific services on their behalf. So far, adoption at scale across businesses has faced difficulties because of data quality, employee distrust, and cost of implementation. In addition, capabilities have raced ahead of leaders’ capacity to imagine how these agents could be used to transform work.

However, as gen AI technologies progress and the next-generation agents emerge, we expect more use cases to be unlocked, deployment costs to decrease, long-tail use cases to become economically viable, and more at-scale automation to take place across a wider range of enterprise processes, employee experiences, and customer interfaces. This evolution will demand investing in strong AI trust and risk management practices and policies as well as platforms for managing and monitoring agent-based systems.

In this interview, McKinsey Digital’s Barr Seitz speaks with senior partners Jorge Amar and Lari Hämäläinen and partner Nicolai von Bismarck to explore the evolution of gen AI agents and how companies can and should implement the technology, where the pools of value lie for the enterprise as a whole. They particularly explore what these developments mean for customer service. An edited transcript of the conversation follows.

Barr Seitz: What exactly is a gen AI agent?

Lari Hämäläinen: When we talk about gen AI agents, we mean software entities that can orchestrate complex workflows, coordinate activities among multiple agents, apply logic, and evaluate answers. These agents can help automate processes in organizations or augment workers and customers as they perform processes. This is valuable because it will not only help humans do their jobs better but also fully digitalize underlying processes and services.

For example, in customer services, recent developments in short- and long-term memory structures enable these agents to personalize interactions with external customers and internal users, and help human agents learn. All of this means that gen AI agents are getting much closer to becoming true virtual workers that can both augment and automate enterprise services in all areas of the business, from HR to finance to customer service. That means we’re well on our way to automating a wide range of tasks in many service functions while also improving service quality.

Barr Seitz: Where do you see the greatest value from gen AI agents?

Jorge Amar: We have estimated that gen AI enterprise use cases  could yield $2.6 trillion to $4.4 trillion annually in value across more than 60 use cases. 1 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. But how much of this value is realized as business growth and productivity will depend on how quickly enterprises can reimagine and truly transform work in priority domains—that is, user journeys, processes across an entire chain of activities, or a function.

Gen-AI-enabled agents hold the promise of accelerating the automation of a very long tail of workflows that would otherwise require inordinate amounts of resources to implement. And the potential extends even beyond these use cases: 60 to 70 percent of the work hours in today’s global economy could theoretically be automated by applying a wide variety of existing technology capabilities, including generative AI, but doing so will require a lot in terms of solutions development and enterprise adoption.

Consider customer service. Currently, the value of gen AI agents in the customer service environment is going to come either from a volume reduction or a reduction in average handling times. For example, in work we published earlier this year, we looked at 5,000 customer service agents using gen AI and found that issue resolution increased by 14 percent an hour, while time spent handling issues went down 9 percent. 2 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023.

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QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

The other area for value is agent training. Typically, we see that it takes somewhere between six to nine months for a new agent to perform at par with the level of more tenured peers. With this technology, we see that time come down to three months, in some cases, because new agents have at their disposal a vast library of interventions and scripts that have worked in other situations.

Over time, as gen AI agents become more proficient, I expect to see them improve customer satisfaction and generate revenue. By supporting human agents and working autonomously, for example, gen AI agents will be critical not just in helping customers with their immediate questions but also beyond, be that selling new services or addressing broader needs. As companies add more gen AI agents, costs are likely to come down, and this will open up a wider array of customer experience options for companies, such as offering more high-touch interactions with human agents as a premium service.

Barr Seitz: What are the opportunities you are already seeing with gen AI agents?

Jorge Amar: Customer care will be one of the first but definitely not the only function with at-scale AI agents. Over the past year, we have seen a lot of successful pilots with gen AI agents helping to improve customer service functions. For example, you could have a customer service agent who is on the phone with a customer and receives help in real time from a dedicated gen AI agent that is, for instance, recommending the best knowledge article to refer to or what the best next steps are for the conversation. The gen AI agent can also give coaching on behavioral elements, such as tone, empathy, and courtesy.

It used to be the case that dedicating an agent to an individual customer at each point of their sales journey was cost-prohibitive. But, as Lari noted, with the latest developments in gen AI agents, now you can do it.

Nicolai von Bismarck: It’s worth emphasizing that gen AI agents not only automate processes but also support human agents. One thing that gen AI agents are so good at, for example, is in helping customer service representatives get personalized coaching not only from a hard-skill perspective but also in soft skills like understanding the context of what is being said. We estimate that applying generative AI to customer care functions could increase productivity by between 30 to 45 percent. 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023.

Jorge Amar: Yes, and in other cases, gen AI agents assist the customer directly. A digital sales assistant can assist the customer at every point in their decision journey by, for example, retrieving information or providing product specs or cost comparisons—and then remembering the context if the customer visits, leaves, and returns. As those capabilities grow, we can expect these gen AI agents to generate revenue through upselling.

[For more on how companies are using gen AI agents, see the sidebar, “A closer look at gen AI agents: The Lenovo experience.”]

Barr Seitz: Can you clarify why people should believe that gen AI agents are a real opportunity and not just another false technology promise?

A closer look at gen AI agents: The Lenovo experience

Three leaders at Lenovo —Solutions and Services Group chief technology officer Arthur Hu, COO and head of strategy Linda Yao, and Digital Workplace Solutions general manager Raghav Raghunathan—discuss with McKinsey senior partner Lari Hämäläinen and McKinsey Digital’s Barr Seitz how the company uses generative AI (gen AI) agents.

Barr Seitz: What existing gen AI agent applications has Lenovo been running and what sort of impact have you seen from them?

Arthur Hu: We’ve focused on two main areas. One is software engineering. It’s the low-hanging fruit to help our people enhance speed and quality of code production. Our people are already getting 10 percent improvements, and we’re seeing that increase to 15 percent as teams get better at using gen AI agents.

The second one is about support. We have hundreds of millions of interactions with our customers across online, chat, voice, and email. We’re applying LLM [large language model]-enhanced bots to address customer issues across the entire customer journey and are seeing some great improvements already. We believe it’s possible to address as much as 70 to 80 percent of all customer interactions without needing to pull in a human.

Linda Yao: With our gen AI agents helping support customer service, we’re seeing double-digit productivity gains on call handling time. And we’re seeing incredible gains in other places too. We’re finding that marketing teams, for example, are cutting the time it takes to create a great pitch book by 90 percent and also saving on agency fees.

Barr Seitz: How are you getting ready for a world of gen AI agents?

Linda Yao: I was working with our marketing and sales training teams just this morning as part of a program to develop a learning curriculum for our organization, our partners, and our key customers. We’re figuring out what learning should be at all levels of the business and for different roles.

Arthur Hu: On the tech side, employees need to understand what gen AI agents are and how they can help. It’s critical to be able to build trust or they’ll resist adopting it. In many ways, this is a demystification exercise.

Raghav Raghunathan: We see gen AI as a way to level the playing field in new areas. You don’t need a huge talent base now to compete. We’re investing in tools and workflows to allow us to deliver services with much lower labor intensity and better outcomes.

Barr Seitz: What sort of learning programs are you developing to upskill your people?

Linda Yao: The learning paths for managers, for example, focus on building up their technical acumen, understanding how to change their KPIs because team outputs are changing quickly. At the executive level, it’s about helping leaders develop a strong understanding of the tech so they can determine what’s a good use case to invest in, and which one isn’t.

Arthur Hu: We’ve found that as our software engineers learn how to work with gen AI agents, they go from basically just chatting with them for code snippets to developing much broader thinking and focus. They start to think about changing the software workflow, such as working with gen AI agents on ideation and other parts of the value chain.

Raghav Raghunathan: Gen AI provides an experiential learning capability that’s much more effective. They can prepare sales people for customer interactions or guide them during sales calls. This approach is having a much greater impact than previous learning approaches. It gives them a safe space to learn. They can practice their pitches ahead of time and learn through feedback in live situations.

Barr Seitz: How do you see the future of gen AI agents evolving?

Linda Yao: In our use cases to date, we’ve refined gen AI agents so they act as a good assistant. As we start improving the technology, gen AI agents will become more like deputies that human agents can deploy to do tasks. We’re hoping to see productivity improvements, but we expect this to be a big improvement for the employee experience. These are tasks people don’t want to do.

Arthur Hu: There are lots of opportunities, but one area we’re exploring is how to use gen AI to capture discussions and interactions, and feed the insights and outputs into our development pipeline. There are dozens of points in the customer interaction journey, which means we have tons of data to mine to understand complex intent and even autogenerate new knowledge to address issues.

Jorge Amar: These are still early days, of course, but the kinds of capabilities we’re seeing from gen AI agents are simply unprecedented. Unlike past technologies, for example, gen AI not only can theoretically handle the hundreds of millions of interactions between employees and customers across various channels but also can generate much higher-quality interactions, such as delivering personalized content. And we know that personalized service is a key driver of better customer service. There is a big opportunity here because we found in a survey of customer care executives we ran that less than 10 percent of respondents  in North America reported greater-than-expected satisfaction with their customer service performance. 4 “ Where is customer care in 2024? ,” McKinsey, March 12, 2024.

Lari Hämäläinen: Let me take the technology view. This is the first time where we have a technology that is fitted to the way humans interact and can be deployed at enterprise scale. Take, for example, the IVR [interactive voice response] experiences we’ve all suffered through on calls. That’s not how humans interact. Humans interact in an unstructured way, often with unspoken intent. And if you think about LLMs [large language models], they were basically created from their inception to handle unstructured data and interactions. In a sense, all the technologies we applied so far to places like customer service worked on the premise that the customer is calling with a very structured set of thoughts that fit predefined conceptions.

Barr Seitz: How has the gen AI agent landscape changed in the past 12 months?

Lari Hämäläinen: The development of gen AI has been extremely fast. In the early days of LLMs, some of their shortcomings, like hallucinations and relatively high processing costs, meant that models were used to generate pretty basic outputs, like providing expertise to humans or generating images. More complex options weren’t viable. For example, consider that in the case of an LLM with just 80 percent accuracy applied to a task with ten related steps, the cumulative accuracy rate would be just 11 percent.

Today, LLMs can be applied to a wider variety of use cases and more complex workflows because of multiple recent innovations. These include advances in the LLMs themselves in terms of their accuracy and capabilities, innovations in short- and long-term memory structures, developments in logic structures and answer evaluation, and frameworks to apply agents and models to complex workflows. LLMs can evaluate and correct “wrong” answers so that you can have much higher accuracy. With an experienced human in the loop to handle cases that are identified as tricky, then the joint human-plus-machine outcome can generate great quality and great productivity.

Finally, it’s worth mentioning that a lot of gen AI applications beyond chat have been custom-built in the past year by bringing different components together. What we are now seeing is the standardization and industrialization of frameworks to become closer to “packaged software.” This will speed up implementation and improve cost efficiency, making real-world applications even more viable, including addressing the long-tail use cases in enterprises.

Barr Seitz: What sorts of hurdles are you seeing in adopting the gen AI agent technology for customer service?

Nicolai von Bismarck: One big hurdle we’re seeing is building trust across the organization in gen AI agents. At one bank, for example, they knew they needed to cut down on wrong answers to build trust. So they created an architecture that checks for hallucinations. Only when the check confirms that the answer is correct is it released. And if the answer isn’t right, the chatbot would say that it cannot answer this question and try to rephrase it. The customer is then able to either get an answer to their question quickly or decide that they want to talk to a live agent. That’s really valuable, as we find that customers across all age groups — even Gen Z — still prefer live phone conversations for customer help and support. .

Jorge Amar: We are seeing very promising results, but these are in controlled environments with a small group of customers or agents. To scale these results, change management will be critical. That’s a big hurdle for organizations. It’s much broader than simply rolling out a new set of tools. Companies are going to need to rewire how functions work so they can get the full value from gen AI agents.

Take data, which needs to be in the right format and place for gen AI technologies to use them effectively. Almost 20 percent of most organizations, in fact, see data as the biggest challenge to capturing value with gen AI. 5 “ The state of AI in 2023: Generative AI’s breakout year ,” McKinsey, August 1, 2023. One example of this kind of issue could be a chatbot sourcing outdated information, like a policy that was used during COVID-19, in delivering an answer. The content might be right, but it’s hopelessly out of date. Companies are going to need to invest in cleaning and organizing their data.

In addition, companies need a real commitment to building AI trust and governance capabilities. These are the principles, policies, processes, and platforms that assure companies are not just compliant with fast-evolving regulations—as seen in the recent EU AI law and similar actions in many countries—but also able to keep the kinds of commitments that they make to customers and employees in terms of fairness and lack of bias. This will also require new learning, new levels of collaboration with legal and risk teams, and new technology to manage and monitor systems at scale.

Change needs to happen in other areas as well. Businesses will need to build extensive and tailored learning curricula for all levels of the customer service function—from managers who will need to create new KPIs and performance management protocols to frontline agents who will need to understand different ways to engage with both customers and gen AI agents.

The technology will need to evolve to be more flexible and develop a stronger life cycle capability to support gen AI tools, what we’d call MLOps [machine learning operations] or, increasingly, gen AI Ops [gen AI operations]. The operating model will need to support small teams working iteratively on new service capabilities. And adoption will require sustained effort and new incentives so that people learn to trust the tools and realize the benefits. This is particularly true with more tenured agents, who believe their own skills cannot be augmented or improved on with gen AI agents. For customer operations alone, we’re talking about a broad effort here, but with more than $400 billion of potential value from gen AI at stake, it’s worth it. 6 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023.

Barr Seitz: Staying with customer service, how will gen AI agents help enterprises?

Jorge Amar: This is a great question, because we believe the immediate impact comes from augmenting the work that humans do even as broader automation happens. My belief is that gen AI agents can and will transform various corporate services and workflows. It will help us automate a lot of tasks that were not adding value while creating a better experience for both employees and customers. For example, corporate service centers will become more productive and have better outcomes and deliver better experiences.

In fact, we’re seeing this new technology help reduce employee attrition. As gen AI becomes more pervasive, we may see an emergence of more specialization in service work. Some companies and functions will lead adoption and become fully automated, and some may differentiate by building more high-touch interactions.

Nicolai von Bismarck: As an example, we’re seeing this idea in practice at one German company, which is implementing an AI-based learning and coaching engine. And it’s already seeing a significant improvement in the employee experience as measured while it’s rolling this out, both from a supervisor and employee perspective, because the employees feel that they’re finally getting feedback that is relevant to them. They’re feeling valued, they’re progressing in their careers, and they’re also learning new skills. For instance, instead of taking just retention calls, they can now take sales calls. This experience is providing more variety in the work that people do and less dull repetition.

Lari Hämäläinen: Let me take a broader view. We had earlier modeled a midpoint scenario when 50 percent of today’s work activities could be automated to occur around 2055. But the technology is evolving so much more quickly than anyone had expected—just look at the capabilities of some LLMs that are approaching, and even surpassing, in certain cases, average human levels of proficiency. The innovations in gen AI have helped accelerate that midpoint scenario by about a decade. And it’s going to keep getting faster, so we can expect the adoption timeline to shrink even further. That’s a crucial development that every executive needs to understand.

Jorge Amar is a senior partner in McKinsey’s Miami office, Lari Hämäläinen is a senior partner in the Seattle office, and Nicolai von Bismarck is a partner in the Boston office. Barr Seitz is director of global publishing for McKinsey Digital and is based in the New York office.

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Google just gave us a tantalizing glimpse into the future of AI agents

• At the Google I/O conference, CEO Sundar Pichai teased where AI is headed next.
• It's going to be all about agents that are better at reasoning and can act on our behalf.
• What if Google did the searching for you?

What if the Google Assistant was actually … an assistant?

It's a question the company finally started answering Tuesday at the 2024 Google I/O conference, where the search giant fired off AI announcements by the dozen.

With Google and other artificial-intelligence companies making advances in how these systems can ingest images, videos, sound, and text, we're beginning to see how these systems are evolving from smart chatbots to more sophisticated tools that can do more of the hard work.

It's something Google CEO Sundar Pichai is thinking a lot about right now.

"I think about AI agents as intelligent systems that show reasoning planning and memory," Pichai said this week at a roundtable with reporters ahead of the developer show.

"They're able to think multiple steps ahead and work across software and systems, all to get something done on your behalf and, most importantly, with your supervision," he added.

In short: AI agents are what have the best chance at taking this technology from a nice-to-have to a need-to-have.

Project Astra

Pichai said Google was "in the very early days" of developing this but promised we'd see glimpses of "agentic direction" across Google's products at the conference. And there were several, but the big one people will be talking about is Project Astra.

Astra is a vision of what the Google Assistant should have been all along. You can also think of it a smarter version of Google Lens, one that uses real-time computer-vision capabilities to let you ask it questions about what you can see and hear around you.

"We've always wanted to build a universal agent that will be useful in everyday life," Google DeepMind's chief, Demis Hassabis, said. "Imagine agents that can see and hear what we do better, understand the context we're in, and respond quickly in conversation, making the pace and quality of interaction feel much more natural."

Google showed a demo of someone holding their phone up with the camera on and asking the AI voice assistant questions about what it was seeing. For example, they pointed it out the window and asked, "What neighborhood do you think I'm in?" Correctly, it located Google's King's Cross office in London.

Related stories

Hassabis stressed that this demo video was recorded in real time. Google got a lot of blowback in December after a Gemini AI-model demo turned out to be edited , so Google needed to emphasize it could really do this — especially after OpenAI showed off a similar demo on Monday.

If there really is no manipulation here, Astra is certainly impressive, and will probably be the big takeaway of the show. But there are other ways these AI agents will emerge in the nearer term.

Google teased a combination of updates that would soon make its Gemini AI chatbot more capable and proactive. Some of this is being unlocked as Google continues to increase the context window , which is the amount of information a large language model can ingest at a time.

Say you want to know something buried deep in a series of very long documents that you don't want to spend hours sifting through. With a large context window, you could share all the documents with Gemini and then ask questions. The model would then answer quickly based on all the information it just ingested.

Tentacles and tailoring

But it's in its legacy products that Google really has an edge when it comes to enabling agentlike qualities.

With its "tentacles" already in many aspects of our lives, from email to search to maps, Google can synthesize all its knowledge about users and the world around them to not just answer queries but also tailor responses.

"My Gemini should really be different than your Gemini," Sissie Hsiao , the head of Gemini and Google Assistant, said.

Later this year, Gemini will be able to plan your vacation with a much more granular level of detail, Hsiao said. The idea is you'll be able to plug in all your specific demands (you like to hike, you hate it when it's too hot, and you're allergic to shellfish) and Gemini will return a detailed itinerary. Chatbots can already do this type of thing, but, if permitted, Gemini will have access to your flight information, travel confirmations in Gmail, and perhaps your hotel, and can use this to inform its answers.

"An AI assistant should be able to solve complex problems, should be able to take actions for you, and also feel very natural and fluid when you engage with it," Hsiao said.

Much of what agents will do is remove steps and shorten tasks. Google is also thinking about this when it comes to Search , as the company evolves its most precious product to spit out answers made using generative AI .

It's rolling out a version of Gemini built specifically for Search, combining its knowledge of the web with the AI model's multimodal abilities and giant context window.

Liz Reid , Google's head of Search, showed an example of Google Lens being used to take a video of a record player. The user asks Gemini why the arm isn't staying in place. Google responds with exact instructions for that specific turntable.

As ever, Google has some confusing branding to solve: Lens, Assistant, Gemini, Astra. Ultimately, though, a lot of this eventually merges together. On Tuesday, Google gave us clues on what this logical conclusion would look like.

As Reid said, "This is a way for Google to do the searching for you."

On February 28, Axel Springer, Business Insider's parent company, joined 31 other media groups and filed a $2.3 billion suit against Google in Dutch court, alleging losses suffered due to the company's advertising practices.

Watch: What is ChatGPT, and should we be afraid of AI chatbots?

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