updating tests

This commit is contained in:
João Moura
2024-09-23 17:45:20 -03:00
parent 493f046c03
commit 2d54b096af
96 changed files with 65055 additions and 30874 deletions

View File

@@ -133,7 +133,7 @@ def test_agent_execute_task():
assert result is not None
assert (
"The calculated area of the circle is approximately 78.5 square centimeters."
"The area of the circle with a radius of 5 cm is approximately 78.5 square centimeters."
== result
)
assert "square centimeters" in result.lower()
@@ -155,7 +155,7 @@ def test_agent_execution():
)
output = agent.execute_task(task)
assert output == "The result of 1 + 1 is 2."
assert output == "The result of the math operation 1 + 1 is 2."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -179,7 +179,7 @@ def test_agent_execution_with_tools():
expected_output="The result of the multiplication.",
)
output = agent.execute_task(task)
assert output == "The result of the multiplication of 3 times 4 is 12."
assert output == "The result of 3 times 4 is 12"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -211,7 +211,7 @@ def test_logging_tool_usage():
tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
)
assert output == "The result of multiplying 3 by 4 is 12."
assert output == "12"
assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
@@ -365,7 +365,7 @@ def test_agent_execution_with_specific_tools():
expected_output="The result of the multiplication.",
)
output = agent.execute_task(task=task, tools=[multiplier])
assert output == "The result of multiplying 3 and 4 is 12."
assert output == "The result of 3 times 4 is 12."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -419,7 +419,7 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
expected_output="The number of customers",
)
output = agent.execute_task(task=task, tools=[comapny_customer_data])
assert output == "42"
assert output == "The company has 42 customers"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1519,9 +1519,8 @@ def test_agent_execute_task_with_custom_llm():
)
result = agent.execute_task(task)
assert len(result.split("\n")) == 3
assert result.startswith(
"Artificial minds,\nSilent, yet they learn and grow,\nFuture in their code."
"Artificial minds,\nLearning, evolving, creating,\nFuture in circuits."
)

View File

@@ -24,7 +24,7 @@ def test_delegate_work():
assert (
result
== 'AI agents are a significant advancement in the field of artificial intelligence, offering immense potential and diverse applications across various sectors. However, the perception that I "hate" them is not entirely accurate. My stance on AI agents is more nuanced and analytical rather than emotional. Here\'s a comprehensive take:\n\n1. **Capabilities and Applications**:\n - **Automation**: AI agents excel in performing repetitive and mundane tasks, allowing human workers to focus on more complex and creative activities.\n - **Efficiency**: They can process vast amounts of data at speeds incomprehensible to humans, leading to quicker decision-making and problem-solving.\n - **Personalization**: In customer service, AI agents provide personalized experiences by analyzing customer data and predicting preferences.\n - **Healthcare**: AI agents assist in diagnosing diseases, recommending treatments, and even predicting outbreaks by analyzing medical data.\n\n2. **Benefits**:\n - **Increased Productivity**: By automating routine tasks, businesses can significantly improve productivity and reduce operational costs.\n - **24/7 Availability**: Unlike human workers, AI agents can operate continuously without breaks, providing constant support and monitoring.\n - **Data-Driven Insights**: AI agents can uncover patterns and insights from data that might be missed by human analysts.\n\n3. **Challenges and Concerns**:\n - **Job Displacement**: There is a legitimate concern about AI agents replacing human jobs, particularly in roles involving routine and predictable tasks.\n - **Bias and Fairness**: AI agents can perpetuate and even amplify existing biases found in the data they are trained on, leading to unfair or discriminatory outcomes.\n - **Transparency**: The decision-making process of AI agents can sometimes be opaque, making it difficult to understand how they arrive at certain conclusions (known as the "black box" problem).\n - **Security**: AI systems can be vulnerable to hacking and other malicious activities, posing significant security risks.\n\n4. **Ethical Considerations**:\n - **Accountability**: Who is responsible when an AI agent makes a mistake? This question remains complex and is a major ethical consideration.\n - **Privacy**: The extensive use of data by AI agents raises concerns about user privacy and the potential for misuse of sensitive information.\n - **Consent**: Users must be informed and provide consent regarding how their data is used by AI agents.\n\n5. **Future Directions**:\n - **Regulation and Governance**: Developing robust regulatory frameworks to oversee the deployment and use of AI agents is crucial.\n - **Continuous Improvement**: Ongoing research is needed to enhance the capabilities of AI agents while addressing their current limitations and ethical concerns.\n - **Collaboration**: Encouraging collaboration between AI agents and human workers can lead to hybrid systems that leverage the strengths of both.\n\nIn summary, while AI agents offer numerous benefits and transformative potential, they also come with a set of challenges and ethical questions that must be carefully managed. My analysis focuses on a balanced view, recognizing both the opportunities and the complexities associated with AI agents. \n\nThus, it\'s not about hating AI agents, but rather critically evaluating their impact and advocating for responsible and fair implementation. This ensures that while we harness their capabilities, we also address and mitigate any negative repercussions.'
== "While I understand the concerns and skepticism surrounding AI agents, I wouldn't say that I hate them. My standpoint is more nuanced. AI agents, which are software entities that perform tasks autonomously using machine learning and other AI technologies, have tremendous potential to revolutionize various sectors.\n\nOn the positive side, AI agents can significantly enhance efficiency and productivity. For example, in customer service, AI agents can handle routine inquiries, allowing human agents to focus on more complex issues. In healthcare, they can assist in diagnosing diseases, thus speeding up the decision-making process and potentially saving lives. In finance, AI agents can automate trading, detect fraudulent activities, and provide personalized financial advice.\n\nHowever, there are legitimate concerns that need to be addressed. One major issue is the ethical implications of deploying AI agents. These include data privacy, biases in decision-making algorithms, and the lack of transparency in how these agents operate. Another concern is the potential job displacement that could result from increased automation. While AI agents can handle many tasks more efficiently than humans, this could lead to significant job losses in certain sectors.\n\nMoreover, there's the matter of reliability and accountability. AI agents, despite their advanced capabilities, are not infallible. They can make mistakes, and when they do, it can be challenging to pinpoint where things went wrong and who is responsible. This raises important questions about oversight and governance.\n\nIn summary, while I am cautious about the unchecked deployment of AI agents due to these ethical and practical concerns, I also recognize their potential to bring about significant positive changes. The key lies in finding a balanced approach that maximizes their benefits while mitigating their risks. This includes rigorous testing, continuous monitoring, and establishing clear ethical guidelines and policies to govern their use. \n\nBy addressing these challenges head-on, we can harness the power of AI agents in a way that is both innovative and responsible."
)
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
assert (
result
== "While it might have circulated that I \"hate\" AI agents, I think it's more nuanced than that. AI agents are incredibly sophisticated pieces of technology that carry immense potential for transforming multiple sectors like healthcare, finance, and customer service.\n\nFirst, let's define what AI agents are. AI agents are autonomous entities designed to perform specific tasks, often mimicking human decision-making and problem-solving abilities. They can range from simple chatbots to complex systems capable of managing large-scale operations.\n\nThe Pros:\n1. Efficiency: AI agents can complete tasks faster and more accurately than humans. For instance, they can instantaneously process large datasets to provide real-time insights.\n2. Scalability: With AI agents, services can be scaled without the proportional increase in cost, which is particularly beneficial for businesses looking to grow without extensive human resource expenditures.\n3. 24/7 Availability: Unlike human workers, AI agents can operate around the clock, increasing productivity and availability, particularly in customer service domains.\n4. Data Handling: AI agents can handle and process data at speeds and volumes that far exceed human capabilities, enabling smarter business decisions and innovations.\n\nHowever, there are some drawbacks and concerns:\n1. Ethical Issues: AI agents often face ethical dilemmas, especially when their decision-making processes are opaque. Bias in AI systems can perpetuate existing inequalities, making transparency and fairness critical issues.\n2. Job Displacement: One significant concern is the potential for AI agents to displace human workers, particularly in industries reliant on routine, repetitive tasks.\n3. Dependency: Over-reliance on AI can lead to skill erosion among human employees and vulnerabilities in cases where technology fails or is compromised.\n4. Security: Given their access to sensitive data, AI agents can be attractive targets for cyber-attacks. Ensuring robust security measures is essential.\n\nSo, while I recognize the transformative power and benefits of AI agents, it's crucial to approach them with a balanced perspective. Adequate governance, ethical considerations, and transparency are needed to harness their potential responsibly. It's not a matter of hating them, but rather advocating for their responsible and fair use in society."
== 'AI agents are specialized software entities that perform tasks autonomously on behalf of users. They leverage artificial intelligence to process inputs, learn from experiences, and make decisions, mimicking human-like behavior. Despite their transformative potential, I don\'t "hate" AI agents; rather, I hold a nuanced view that acknowledges both their advantages and limitations.\n\nAdvantages of AI Agents:\n1. **Efficiency and Productivity**: AI agents can handle repetitive tasks efficiently, freeing up human workers to focus on more complex and creative activities.\n2. **24/7 Operation**: Unlike humans, AI agents can work around the clock without breaks, significantly increasing productivity and service availability.\n3. **Data Processing**: They can process and analyze vast amounts of data quickly and accurately, supporting better decision-making.\n4. **Personalization**: AI agents can tailor services and recommendations based on user behavior and preferences, improving customer satisfaction.\n\nLimitations and Concerns:\n1. **Ethical Issues**: The deployment of AI agents raises concerns about data privacy, surveillance, and the potential for bias in decision-making algorithms.\n2. **Job Displacement**: There is legitimate concern about AI agents replacing human jobs, especially in industries where tasks are routine and repetitive.\n3. **Dependence on Data Quality**: AI agents\' performance hinges on the quality and quantity of data they are trained on. Poor data quality can lead to erroneous outcomes.\n4. **Complexity in Implementation**: Developing and maintaining AI agents requires significant technical expertise and resources. Problems can arise from their complexity, leading to potential failures.\n\nIn conclusion, while I don\'t "hate" AI agents, I am cautious of their broad and uncritical adoption. Its essential to strike a balance between leveraging their capabilities and addressing the ethical, social, and technical challenges they present.'
)
@@ -52,7 +52,7 @@ def test_ask_question():
assert (
result
== 'As a researcher specialized in technology, particularly in artificial intelligence (AI) and AI agents, my perspective is shaped by the extensive analysis and study I engage in daily. AI agents are neither inherently good nor bad; they are tools with the potential for both positive and negative impacts depending on how they are designed, implemented, and used.\n\nI do not "hate" AI agents. In fact, I find them fascinating and hold an appreciation for their capabilities and the potential they have to transform various sectors, from healthcare and education to finance and entertainment. AI agents can offer significant benefits, such as improving efficiency, personalizing user experiences, and even tackling complex problems that are beyond human capacity to solve alone.\n\nHowever, it is essential to approach AI with a critical eye. There are legitimate concerns around privacy, security, ethical use, and the socio-economic implications of AI deployment. Ensuring that AI is developed responsibly and is aligned with ethical standards is crucial for minimizing potential negatives.\n\nIn summary, my stance on AI agents is informed by a balanced view that recognizes both their potential and the necessity for responsible stewardship. The enthusiasm I have for their capabilities is tempered by a commitment to understanding and addressing the challenges they present.'
== "As a researcher specializing in technology and AI, I don't hate AI agents. In fact, I find them incredibly fascinating and beneficial. AI agents have the potential to transform various industries, improve efficiencies, and offer new solutions to complex problems. Their ability to learn, adapt, and perform tasks that were once thought to require human intelligence is remarkable. While it's important to consider ethical implications and ensure that AI systems are designed and deployed responsibly, I believe their overall positive impact on society and technology is significant. So to clarify, I don't hate AI agents; rather, I am quite enthusiastic about their potential and the advancements they bring to the field of technology."
)
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
assert (
result
== "As an expert researcher specialized in technology and AI, I do not harbor feelings of hate towards AI agents. In fact, the notion of hate or love for a technology is somewhat misplaced. AI agents are tools created to solve specific problems and enhance our capabilities. They can transform industries, improve efficiencies, and even save lives in fields such as healthcare. My enthusiasm and interest in AI agents come from their potential to innovate and drive progress. While it is essential to be mindful of ethical considerations and responsible use, it is equally important to remain objective. My goal as a researcher is to contribute to balanced and insightful analysis, exploring both the benefits and challenges of AI, and not to personalize or emotionally charge the discussion about technological tools."
== "As an expert researcher specialized in technology and AI, my perspective on AI agents is shaped by both their potential and limitations. AI agents are tools designed to perform tasks, analyze data, and assist in various domains efficiently and accurately. They have the capability to revolutionize industries by automating complex processes, enhancing decision-making, and providing personalized experiences. For instance, in healthcare, AI agents can help in diagnosing diseases with high precision, while in finance, they can predict market trends and prevent fraud.\n\nHowever, my appreciation for AI agents does not mean I am blind to their challenges. There are valid concerns related to privacy, ethical use, and the potential displacement of jobs. The development and deployment of AI should be approached with caution, ensuring transparency, fairness, and accountability.\n\nIn conclusion, I value the advancements AI agents bring to the table and acknowledge their profound impact on society. My interest lies in leveraging their potential responsibly while addressing the associated ethical and societal challenges. So, while I love the capabilities and innovations brought forth by AI agents, I remain critically aware of the need for responsible development and use."
)
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
assert (
result
== "AI agents, like any technology, have both significant advantages and notable challenges. Initially, the skepticism surrounding AI agents might stem from concerns about their implementation, ethical considerations, or potential impact on jobs. However, let's delve into a comprehensive analysis of their roles and implications.\n\nFirstly, AI agents are software entities that perform tasks autonomously on behalf of a user or another program with some degree of intelligence and learning capability. These agents can interpret data, learn from patterns, and even make decisions based on the information they process. They are essential across various sectors, including healthcare, finance, customer service, and logistics.\n\nPositive Aspects:\n1. **Efficiency and Productivity:** AI agents can process large volumes of data faster than humans, enabling quicker decision-making. This efficiency can significantly boost productivity in organizations by automating repetitive tasks, allowing human workers to focus on more strategic activities.\n\n2. **24/7 Operation:** Unlike humans, AI agents can work around the clock without fatigue. This capability is crucial for industries that require continuous operation, such as monitoring systems in cybersecurity or customer service through chatbots.\n\n3. **Personalization:** AI agents can tailor experiences to individual users by learning from their behavior. For instance, recommendation systems used by services like Netflix or Amazon provide personalized content, enhancing user satisfaction and engagement.\n\n4. **Accuracy and Precision:** AI agents, when properly trained, can perform tasks with a high degree of accuracy, reducing the likelihood of human error. This precision is especially valuable in fields such as medical diagnostics, where accurate analysis can be life-saving.\n\nChallenges and Concerns:\n1. **Ethical Issues:** The deployment of AI agents raises ethical questions regarding privacy, data security, and potential biases in decision-making. It is essential to ensure that AI systems are transparent, fair, and respect user privacy.\n\n2. **Job Displacement:** As AI agents automate more tasks, there is a genuine concern about job loss, particularly in roles that involve routine and repetitive tasks. It is critical to address this issue by reskilling and upskilling the workforce to adapt to the changing job landscape.\n\n3. **Dependence on Data:** AI agents rely heavily on large datasets to learn and make decisions. If the data is biased or incomplete, the AIs performance and reliability can be compromised. This dependence also raises questions about data ownership and governance.\n\n4. **Complexity and Accountability:** Understanding and managing AI agents can be complex, and determining accountability in the event of failures or erroneous decisions can be challenging. Developing robust frameworks for monitoring and accountability is crucial.\n\nIn conclusion, while there are valid concerns associated with AI agents, they also offer transformative potential in improving efficiency, personalization, and accuracy in various applications. It is important to engage in balanced discussions, address ethical considerations, and develop policies to mitigate potential negative impacts while leveraging the benefits of AI technology. Hence, my stance is not of outright hatred, but one of cautious optimism, advocating for responsible development and deployment of AI agents."
== "It's interesting that you've heard I dislike AI agents; I suspect there may have been a miscommunication. My thoughts on AI agents are more nuanced than a simple like or dislike.\n\nAI agents can be incredibly powerful tools with the potential to drastically transform various industries. Their ability to automate tasks, analyze vast amounts of data, and make predictions can lead to significant improvements in efficiency and innovation. For instance, in healthcare, AI agents can assist in diagnosing diseases by quickly analyzing medical images. In finance, they can help in fraud detection by swiftly recognizing suspicious patterns in transactions. The applications are virtually limitless and continually expanding.\n\nHowever, there are concerns that need to be addressed, which might have led to a perception that I \"hate\" AI agents. One concern is the ethical implications surrounding their deployment. Issues such as data privacy, algorithmic bias, and the potential for job displacement are significant. For example, if an AI system is trained on biased data, it may make unfair or discriminatory decisions, perpetuating existing societal inequalities. Moreover, as AI agents take over repetitive tasks, there's a real risk that many jobs could become obsolete, causing economic disruption.\n\nAdditionally, there's the matter of accountability. When an AI agent makes a decision, it's not always clear who is responsible if something goes wrong. This opacity poses challenges for regulatory frameworks and trust in these systems. \n\nBalancing the tremendous benefits AI agents can provide with the ethical and practical challenges they introduce is crucial. Rather than viewing AI agents as something to be liked or disliked, I see them as tools that need thoughtful integration and rigorous oversight to maximize their positive impact and minimize their risks. Therefore, while I am enthusiastic about the potential of AI agents, I advocate for a cautious and responsible approach to their development and deployment."
)
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
assert (
result
== "While I understand there are mixed feelings about AI agents, I do not hate them. In fact, I see significant potential in AI and AI agents to transform various industries and improve our daily lives. My enthusiasm for AI comes from appreciating the advancements it's bringing, such as personalized recommendations, improved diagnostics in healthcare, and more efficient business processes.\n\nHowever, it's essential to approach AI with a balanced perspective. Ethical considerations, privacy concerns, and the potential for job displacement are critical issues that need to be addressed. I believe in responsible AI development, where the focus is on maximizing benefits while minimizing potential harms.\n\nSo, in short, I love the potential and capabilities of AI agents when developed and implemented responsibly and ethically."
== "As an expert researcher in technology with a specialization in AI and AI agents, my perspective is rooted in my deep understanding of their capabilities and potential. AI agents, like any technology, are tools that can be used for both beneficial and harmful purposes. Personally, I do not hate AI agents; rather, I recognize their immense potential to transform industries, improve efficiencies, and solve complex problems. However, I also acknowledge that they come with challenges that need to be carefully managed, such as ethical considerations, privacy concerns, and the potential for job displacement.\n\nThe reason you might have heard that I love them is likely because I am passionate about the potential that AI agents hold for advancing technology and aiding humanity. I believe that with responsible development, transparent governance, and thoughtful integration, AI agents can indeed bring about positive change. My enthusiasm should not be misconstrued as blind love but rather as a measured appreciation for their capabilities and a commitment to navigating their complexities responsibly."
)

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that were once thought to require human intelligence is remarkable. While it's
important to consider ethical implications and ensure that AI systems are designed
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headers:
Accept:
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Content-Type:
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User-Agent:
@@ -119,7 +93,7 @@ interactions:
Content-Type:
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Date:
- Mon, 23 Sep 2024 06:26:26 GMT
- Mon, 23 Sep 2024 19:38:38 GMT
status:
code: 200
message: OK
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content-type:
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Task: Write an article about the history of AI and its most important events.\n\nThis
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is the context you''re working with:\n- Autonomous AI Agents in Creative Industries:
An exploration of how AI is transforming the creative sector, from automated
content generation to AI-driven design tools. Unique aspects include the ethical
implications and the collaboration between human creativity and machine learning.\n\n-
AI in Predictive Healthcare: A look at how artificial intelligence is being
used to predict diseases before they manifest, optimize treatment plans, and
improve patient outcomes. This is interesting because it integrates big data,
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AI and Ethical Decision Making: An examination of how AI can be programmed to
make ethical decisions, particularly in autonomous systems like self-driving
cars and military applications. This topic is compelling due to its philosophical,
social, and technical challenges.\n\n- AI for Sustainable Development: Investigating
how AI technologies are being leveraged to address environmental challenges,
such as climate change, resource management, and wildlife conservation. This
is significant due to the pressing need for sustainable solutions and the innovative
use of technology.\n\n- AI in Personal Finance: How AI-driven applications are
reshaping personal finance management, from robo-advisors to fraud detection
and personalized financial planning. The unique angle here is the blend of advanced
analytics, user behavior insights, and financial technology (fintech).\n\n----------\n\n1.
**The Dartmouth Conference (1956):**\n - **Event Description:** The Dartmouth
Conference, held in the summer of 1956, is widely considered the birthplace
of artificial intelligence as a field. Organized by John McCarthy, Marvin Minsky,
Nathaniel Rochester, and Claude Shannon, this event brought together leading
researchers to discuss the development of intelligent machines.\n - **Impact:**
The conference laid the foundation for AI research and formalized the term \"artificial
intelligence.\" It set the stage for future advancements and established AI
as a distinct academic discipline.\n\n2. **Development of the Perceptron (1957):**\n -
**Event Description:** Frank Rosenblatt developed the Perceptron, an early version
of a neural network inspired by biological neurons. The Perceptron was capable
of pattern recognition and learning from data.\n - **Impact:** This development
marked significant progress in machine learning and neural networks. Although
initial excitement waned due to limitations, it eventually led to the resurgence
of interest in neural networks and deep learning in later decades.\n\n3. **IBM''s
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Deep Blue, a chess-playing computer, defeated the reigning world chess champion,
Garry Kasparov, in a six-game match.\n - **Impact:** This event demonstrated
the potential of AI to perform complex, human-like tasks at a high level. It
also spurred public interest and investment in AI, highlighting the advances
in machine learning and computational power.\n\n4. **Introduction of Google''s
AlphaGo (2016):**\n - **Event Description:** Google''s AlphaGo, developed
by DeepMind, defeated Lee Sedol, one of the world''s greatest Go players, in
a five-game match.\n - **Impact:** AlphaGo''s victory showcased the potential
of deep learning and reinforcement learning in mastering complex tasks that
were previously thought to be beyond the reach of machines. This event underscored
the rapid advancements in AI and accelerated research and development in various
AI subfields.\n\n5. **The Rise of Transformer Models (2017):**\n - **Event
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a paper titled \"Attention Is All You Need.\" This architecture revolutionized
natural language processing (NLP) by enabling more efficient and effective handling
of sequential data.\n - **Impact:** Transformers have since become the foundation
for state-of-the-art NLP models, including BERT and GPT. These models have significantly
advanced language understanding, translation, and generation tasks, paving the
way for more sophisticated AI applications in diverse industries, including
creative content generation and personal finance.\n\nEach of these events has
played a critical role in shaping the evolution of AI technology, influencing
subsequent developments and opening new frontiers in various sectors.\n\nBegin!
This is VERY important to you, use the tools available and give your best Final
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is the context you''re working with:\n1. **Transforming Industries with AI Agents:
Case Studies and Future Implications**\n - *What makes it unique and interesting:*
This article could explore significant real-world applications of AI agents
in various industries such as healthcare, finance, retail, and transportation.
Highlighting successful case studies will provide readers with a clear understanding
of how AI agents are currently revolutionizing these fields and what future
advancements might look like.\n\n2. **Ethics and AI: Navigating the Moral Dilemmas
of Autonomous Agents**\n - *What makes it unique and interesting:* This article
delves into the ethical concerns and moral dilemmas associated with deploying
AI agents. By discussing topics like bias in AI, decision-making accountability,
and the potential societal impacts, the article can engage readers in a crucial
conversation about responsible AI development.\n\n3. **The Evolution of AI Agents:
From Rule-Based Systems to Reinforcement Learning**\n - *What makes it unique
and interesting:* Tracing the historical development of AI agents offers readers
a comprehensive view of how these technologies have evolved. Comparing early
rule-based systems with modern reinforcement learning techniques can highlight
significant advancements and provide context for current trends and future possibilities
in AI research.\n\n4. **AI in Personal Assistants: How Intelligent Agents are
Changing Daily Life**\n - *What makes it unique and interesting:* This article
can focus on how AI-powered personal assistants like Siri, Alexa, and Google
Assistant are impacting everyday life. Covering aspects such as efficiency in
time management, accessibility, and improving quality of life, the article can
show readers the practical benefits and challenges associated with these ubiquitous
technologies.\n\n5. **The Future of Work: AI Agents and Job Transformation**\n -
*What makes it unique and interesting:* Focusing on the profound ways AI agents
are expected to transform the job market, this article can examine both the
opportunities and challenges. Discussing topics like job displacement, the creation
of new job categories, and the skills workers will need to thrive in an AI-driven
economy will provide valuable insights for readers concerned about the future
of work.\n\n----------\n\n1. **Turing Test (1950)**\n - Alan Turing published
a paper titled \"Computing Machinery and Intelligence\" in which he proposed
the idea of the Turing Test. This test was designed to evaluate whether a machine
could exhibit intelligent behavior equivalent to, or indistinguishable from,
that of a human. The Turing Test has had a lasting impact on AI research and
is often considered the cornerstone of AI philosophy.\n\n2. **Dartmouth Workshop
(1956)**\n - The Dartmouth Conference, held in the summer of 1956, is widely
considered the birth of AI as a field of study. During this workshop, the term
\"Artificial Intelligence\" was coined by John McCarthy. This event brought
together the leading minds in computing and laid the foundational goals and
principles for AI research.\n\n3. **Development of Expert Systems (1970s-1980s)**\n -
The development of expert systems represented a major leap in AI technology.
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in specific domains, such as medical diagnosis and financial planning. Prominent
examples include MYCIN and DENDRAL. Expert systems demonstrated the practical
utility of AI in real-world applications and contributed significantly to commercial
interests in AI.\n\n4. **Deep Learning Breakthrough (2012)**\n - A significant
milestone in AI was the advent of deep learning, particularly marked by AlexNet''s
victory in the ImageNet Large Scale Visual Recognition Challenge in 2012. Developed
by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, AlexNet significantly
outperformed other approaches in image recognition tasks. This success spurred
widespread interest and investment in deep learning, leading to major advancements
in fields such as computer vision, natural language processing, and autonomous
systems.\n\n5. **AlphaGo Defeats Lee Sedol (2016)**\n - In March 2016, Google
DeepMind''s AlphaGo program made headlines by defeating Lee Sedol, one of the
world''s top Go players, in a five-game match. Go is a highly complex board
game with more possible moves than there are atoms in the universe, making it
a major challenge for AI. AlphaGo''s victory was seen as a landmark achievement
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in tackling complex, strategic problems.\n\nBegin! This is VERY important to
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@@ -227,7 +227,7 @@ def test_crew_creation():
result = crew.kickoff()
expected_string_output = "**1. The Rise of Autonomous AI Agents in the Business Sector**\n\nThe business sector is experiencing a radical transformation with the rise of autonomous AI agents taking on roles traditionally filled by human employees. These intelligent systems are revolutionizing customer service, sales, and administrative tasks by offering unmatched efficiency and cost savings. Yet, this shift brings forward ethical and practical challenges that need careful navigation. Companies like IPsoft have pioneered the deployment of AI agents, demonstrating both the immense potential and the hurdles encountered. By automating routine tasks, businesses can reallocate human talent towards more strategic initiatives, driving innovation while maintaining operational excellence.\n\n**2. AI and Creativity: Can Machines Be Truly Creative?**\n\nThe debate over whether machines can truly be creative is gaining momentum as AI algorithms begin to compose music, create visual art, and author literature. This exploration into AI's creative potential raises profound questions about the nature of creativity and consciousness. AI systems like OpenAI's GPT-3 have crafted compelling narratives and artistic expressions, showcasing glimpses of what the future holds for the fusion of technology and creativity. Highlighting such AI-generated works provides a tantalizing view into a future where humans and machines could collaborate in groundbreaking artistic ventures, reshaping our understanding of creativity itself.\n\n**3. Ethical AI: Navigating the Moral Landscape of Artificial Intelligence**\n\nAs AI technologies advance, the need for ethical considerations becomes increasingly critical. This topic delves into the frameworks and guidelines being developed to ensure that AI systems align with human values and morals. The ethical landscape of AI intersects with various fields including law, philosophy, and social justice, prompting a multidisciplinary approach to responsible AI development. Featuring insights from AI ethicists, policymakers, and technologists, the article explores the balance between innovation and ethical responsibility. By navigating these moral complexities, society can harness the benefits of AI while mitigating potential risks and ensuring equitable outcomes.\n\n**4. AI in Healthcare: Transforming Diagnosis and Treatment**\n\nAI is on the brink of revolutionizing healthcare, offering transformative changes in diagnosis and treatment. From predictive diagnostics to personalized treatment plans, and even robotic surgery, AI technologies are improving patient outcomes and reducing healthcare costs. Virtual health assistants powered by AI are broadening access to care, making healthcare more inclusive and efficient. Real-world examples, such as IBM Watson's ability to diagnose complex medical conditions, underscore the profound impact AI can have in the medical field. By examining these advancements, the article provides a comprehensive look at how AI is poised to redefine the future of healthcare.\n\n**5. AI and Cybersecurity: The Arms Race of the Future**\n\nIn the ever-evolving world of cybersecurity, AI is emerging as both a potent defender and a formidable adversary. AI technologies are now capable of identifying and responding to threats in real-time, offering robust protection against cyber-attacks. However, malicious actors are also harnessing AI to launch more sophisticated and stealthy attacks, leading to an ongoing arms race in the digital landscape. The relevance of this topic is underscored by the multitude of high-profile security breaches, driving public and private sectors to invest heavily in AI-driven cybersecurity measures. Through expert insights and case studies, this article will explore the capabilities of AI in safeguarding sensitive data and anticipating future threats, painting a vivid picture of the cybersecurity landscape of tomorrow."
expected_string_output = "1. **AI in Healthcare Diagnostics**\n\nIn the realm of healthcare, AI is paving the way for groundbreaking advancements in diagnostic processes. By leveraging sophisticated deep learning algorithms, AI can analyze medical imaging and patient data with unprecedented accuracy, offering a transformative approach to identifying diseases early on. This innovative use of AI significantly reduces the margin of diagnostic errors and expedites the diagnosis process. For instance, conditions such as cancer or neurological disorders, which necessitate rapid and accurate detection for effective treatment, can be diagnosed much earlier and more accurately with AI, potentially transforming patient outcomes and elevating the standard of care.\n\n2. **Autonomous Agents in Financial Trading**\n\nThe financial trading industry is witnessing a revolutionary shift with the introduction of AI-powered autonomous agents. These advanced systems are designed to analyze market trends, execute trades, and manage investment portfolios in real-time without the need for human intervention. By processing vast amounts of financial data at speeds unattainable by humans, these AI agents can identify patterns, predict market movements, and make informed trading decisions that maximize returns and minimize risks. This technological leap not only enhances the efficiency of financial markets but also democratizes access to sophisticated trading strategies, leveling the playing field for individual investors.\n\n3. **AI-Powered Personal Assistants Beyond Siri and Alexa**\n\nAI personal assistants are evolving beyond simple voice commands to become indispensable parts of our daily lives, offering tailored, context-aware recommendations and handling complex tasks. Imagine a personal assistant that not only manages your schedule but also understands your preferences to make personalized online shopping suggestions or provides emotional support during stressful times. These advancements signify a deeper integration of AI into our routines, enhancing productivity and revolutionizing how we interact with technology. The potential impact on personal digital management is immense, promising a future where AI seamlessly assists in various aspects of life, from mundane chores to emotional well-being.\n\n4. **Ethical Implications of AI in Surveillance**\n\nAs AI technology advances, its application in surveillance raises critical ethical concerns. AI-driven tools like facial recognition and predictive policing can significantly enhance security measures but also pose substantial risks to privacy and civil liberties. The deployment of such technologies necessitates a careful balance between security and the protection of individual rights, as well as fostering societal trust. This tension between technological capabilities and ethical standards underscores the need for ongoing dialogue and robust regulatory frameworks to ensure that the benefits of AI-driven surveillance do not come at the expense of fundamental freedoms and societal trust.\n\n5. **AI and Creativity: From Art to Music**\n\nAI is not just a tool for analytical tasks; it is also making waves in the creative industries by producing original artworks, music compositions, and literary pieces. This fascinating blend of technology and creativity raises profound questions about the nature of creativity and the role of human artists in an era where machines can autonomously generate culturally significant works. AI's ability to mimic and innovate upon human creativity challenges traditional notions of authorship and artistic value, pushing the boundaries of what technology can achieve and offering fresh perspectives on the future of creative expression. The implications for the arts, as well as the cultural industries reliant on human creativity, are profound, suggesting a future where collaboration between humans and AI could lead to unprecedented creative heights."
assert str(result) == expected_string_output
assert result.raw == expected_string_output
@@ -295,7 +295,7 @@ def test_hierarchical_process():
assert (
result.raw
== "1. **The Rise of AI in Healthcare: Transformations and Challenges**\n The advent of artificial intelligence is profoundly transforming the healthcare landscape, ushering in a new era of diagnostics and personalized treatment plans. AI is not only enhancing the accuracy and speed of diagnostics through advanced medical imaging and predictive analytics but also empowering physicians to devise highly tailored treatment plans based on individual patient data. Despite these substantial benefits, the integration of AI into healthcare systems is not without its challenges, including issues related to data privacy, the need for substantial infrastructural changes, and the necessity of re-skilling healthcare professionals to work alongside AI technologies. As we navigate these transformations, it is crucial to address these challenges comprehensively to fully harness the potential of AI in improving patient outcomes.\n\n2. **From Chatbots to Autonomous Agents: The Evolution of AI-driven Customer Service**\n Customer service has undergone a significant evolution with the advent of AI, transitioning from basic chatbots to sophisticated autonomous agents. Early chatbots provided rudimentary assistance, often limited to scripted responses. However, the advent of advanced natural language processing and machine learning algorithms has birthed autonomous agents capable of understanding context, generating human-like responses, and even predicting customer needs. These intelligent agents are now handling complex customer inquiries, providing personalized interactions, and operating across multiple channels seamlessly. Companies deploying these technologies are witnessing increased customer satisfaction and operational efficiency, marking a pivotal shift in the realm of customer service.\n\n3. **AI for Good: How Artificial Intelligence is Addressing Global Challenges**\n Artificial intelligence is increasingly becoming a powerful tool in addressing some of the world's most pressing challenges, from climate change and poverty to healthcare disparities. One transformative example is AIs capability to analyze vast datasets to predict natural disasters, enabling timely interventions and life-saving measures. Projects like AI-driven agricultural systems are optimizing crop yields to combat hunger, while intelligent algorithms are being used to identify and bridge gaps in healthcare delivery in underserved regions. Through these innovative applications, AI is not just a technological advancement but a catalyst for global good, proving that technology can indeed play a pivotal role in solving critical human issues.\n\n4. **The Future of Work: AI and its Implications on Employment and Skillsets**\n As artificial intelligence continues to develop, its implications on the job market are profound and multifaceted. AI is automating routine and repetitive tasks, prompting a shift towards jobs that require more complex problem-solving, creativity, and emotional intelligence. This evolution is creating an imperative for a new set of skills, including proficiency in AI tools, data analysis, and adaptability to new technologies. The role of education and policy cannot be overstated in this transition, as they must adapt to prepare the workforce for these emerging requirements. Lifelong learning and reskilling are becoming essential as AI redefines existing job roles and creates entirely new ones.\n\n5. **Ethical AI: Navigating the Principles and Practices of Responsible AI Development**\n The development and deployment of artificial intelligence bring a suite of ethical considerations that are critical to its responsible use. Foundational principles such as fairness, accountability, and transparency must guide AI development to prevent biases and ensure equitable outcomes. For instance, algorithmic transparency is crucial to understanding and mitigating biases that may arise from training data. Furthermore, accountability measures are necessary to address the impacts of AI decisions, particularly in high-stakes areas like criminal justice and employment. Current guidelines and case studies highlight the importance of these ethical frameworks, urging developers and policymakers to adopt practices that promote responsible AI usage, thereby fostering public trust and maximizing societal benefits."
== "1. **The Rise of Autonomous AI Agents: Transforming Industries and Everyday Life**\n - Autonomous AI agents are revolutionizing the landscape of various industries, from healthcare to logistics, by making independent decisions that enhance efficiency and accuracy. In healthcare, these agents can diagnose diseases with remarkable precision, while in finance, they're optimizing trading strategies and risk management in real-time. Customer service has seen a transformation with AI agents offering 24/7 support, significantly improving user satisfaction. Despite their numerous advantages, the deployment of autonomous AI comes with challenges such as ethical considerations, the need for transparency, and mitigating biases in decision-making algorithms. As the technology evolves, understanding how these agents function and their potential to reshape our everyday life is becoming increasingly crucial, sparking widespread curiosity and debate.\n\n2. **Ethical Considerations in AI Development: Balancing Innovation and Responsibility**\n - The rapid advancement of AI brings forth critical ethical considerations that need to be addressed to ensure responsible development. Issues like algorithmic bias, privacy violations, and the displacement of jobs present significant challenges. However, many companies and researchers are pioneering efforts to tackle these ethical dilemmas. For instance, some are developing frameworks for bias detection and mitigation, while others are advocating for stronger data privacy regulations. The ongoing discourse around balancing innovation with responsibility is not only relevant but essential, as it shapes the future trajectory of AI in our society. This article will provide a deep dive into these ethical challenges and highlight exemplary initiatives, making it a compelling read for developers, policymakers, and concerned citizens alike.\n\n3. **AI in Creative Fields: Revolutionizing Art, Music, and Writing**\n - AI has made significant inroads in creative fields, sparking a new era of artistic expression and collaboration. From AI-generated paintings that fetch high prices at auctions to algorithms composing symphonies and writing novels, the intersection of creativity and technology is pushing boundaries. Renowned artists and musicians are now working alongside sophisticated AI systems, creating works that were previously unimaginable. These collaborations challenge our perceptions of creativity and originality, inviting debates about the role of human intuition versus machine learning. Highlighting these fascinating stories of innovation, this article will captivate readers who are intrigued by the limitless potential of AI to inspire and transform the arts.\n\n4. **AI-Powered Personal Assistants: The Next Generation of Smart Living**\n - AI-powered personal assistants such as Google Assistant, Alexa, and Siri are at the forefront of the next generation of smart living, becoming more intuitive and context-aware by the day. These assistants are no longer just about setting reminders or playing music; they are evolving to understand and anticipate our needs, manage complex tasks, and even provide personalized recommendations. They are seamlessly integrating into various aspects of daily life, from home automation to health management. This article will explore the latest advancements and future directions of AI-powered personal assistants, offering readers an insight into how these innovations are shaping a smarter, more connected world.\n\n5. **The Future of Work: How AI and Automation are Reshaping the Job Market**\n - The advent of AI and automation is fundamentally altering the job market, raising both opportunities and concerns. While certain repetitive and mundane jobs are at risk of being automated, new industries and job categories are emerging, requiring skills in AI management and oversight. Educational institutions and individuals are actively adapting by developing curricula and acquiring skills that align with these technological advancements. This article will provide a comprehensive analysis of the job roles most susceptible to automation and those that are emerging as a result of AI. It will also delve into how various sectors are preparing for this transition, making it a vital read for anyone concerned about navigating career futures in an AI-driven world."
)
@@ -401,7 +401,7 @@ def test_crew_with_delegating_agents():
assert (
result.raw
== "In today's rapidly evolving technological landscape, AI agents stand at the forefront of innovation, reshaping industries with their unparalleled capabilities. These intelligent systems, capable of performing complex tasks autonomously, are revolutionizing a myriad of sectors—from healthcare, where they assist in diagnosing and personalizing treatment plans, to finance, where they enhance trading strategies and fraud detection. The significance of AI agents lies in their ability to learn and adapt, continuously improving their performance through advanced machine learning algorithms. As businesses strive for optimization and efficiency, AI agents emerge as indispensable allies, driving progress and heralding a new era of intelligent automation."
== "1. **Introduction**\nAI Agents, often referred to as autonomous agents, are sophisticated software programs capable of performing tasks and making decisions independently. By leveraging advanced algorithms and vast datasets, these agents can analyze complex information and execute actions with minimal human intervention. The significance of AI Agents lies in their ability to optimize operations, provide personalized recommendations, and enhance decision-making processes across various industries.\n\n2. **Applications and Impact**\nFrom managing customer service inquiries through chatbots to optimizing supply chain logistics and even assisting in medical diagnostics, the potential applications of AI Agents are vast and transformative. In the financial sector, for instance, AI Agents can predict market trends and provide investment advice, while in healthcare, they can analyze patient data to support early diagnosis and treatment plans. The integration of AI Agents in various fields not only improves efficiency but also opens up new opportunities for innovation and growth.\n\n3. **Challenges and Ethical Considerations**\nDespite their numerous advantages, the deployment of AI Agents is not without challenges. Issues such as data privacy, algorithmic biases, and the potential for job displacement raise significant ethical and societal questions. Ensuring that AI Agents operate transparently and fairly requires robust regulatory frameworks and continuous monitoring. Additionally, as AI Agents increasingly participate in decision-making processes, it is crucial to maintain a balance between automation and human oversight to prevent unintended consequences.\n\n4. **Future Prospects**\nAs technology advances, the capabilities of AI Agents are expected to grow exponentially. Future developments may include more sophisticated natural language processing, enhanced emotional intelligence, and greater adaptability to diverse environments. These advancements could enable AI Agents to perform more complex tasks, such as creative problem-solving and strategic planning. Ultimately, the evolution of AI Agents will have a profound impact on various aspects of our lives, driving progress and innovation while reshaping the future of work and human interaction with technology."
)
@@ -612,7 +612,7 @@ def test_sequential_async_task_execution_completion():
sequential_result = sequential_crew.kickoff()
assert sequential_result.raw.startswith(
"The history of artificial intelligence (AI) is marked by a series of pivotal events that have shaped its progress and applications across diverse sectors."
"The history of Artificial Intelligence (AI) is a fascinating journey marked by pioneering breakthroughs and revolutionary innovations."
)
@@ -639,7 +639,9 @@ def test_single_task_with_async_execution():
)
result = crew.kickoff()
assert result.raw.startswith("- The rise of AI in healthcare diagnostics.")
assert result.raw.startswith(
"- AI in healthcare: Revolutionizing diagnostics and personalized treatment."
)
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1162,7 +1164,7 @@ def test_task_with_no_arguments():
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
assert result.raw == "The total number of sales is 75."
assert result.raw == "75"
def test_code_execution_flag_adds_code_tool_upon_kickoff():
@@ -1272,9 +1274,9 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
assert result.raw == "Howdy!"
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prompt_tokens=2492,
completion_tokens=142,
total_tokens=2701,
prompt_tokens=2550,
completion_tokens=151,
successful_requests=5,
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@@ -2373,7 +2375,7 @@ def test_conditional_task_last_task_when_conditional_is_true():
)
result = crew.kickoff()
assert result.raw.startswith(
"1. **The Rise of AI Agents in Customer Service: Enhancing User Experience Through Intelligent Automation**"
"1. **The Role of AI Agents in Revolutionizing Customer Service**"
)

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@@ -321,8 +321,8 @@ def test_output_json_hierarchical():
manager_llm="gpt-4o",
)
result = crew.kickoff()
assert '{"score": 4}' == result.json
assert result.to_dict() == {"score": 4}
assert '{"score": 5}' == result.json
assert result.to_dict() == {"score": 5}
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@@ -474,7 +474,7 @@ def test_output_json_to_another_task():
crew = Crew(agents=[scorer], tasks=[task1, task2])
result = crew.kickoff()
assert '{"score": 5}' == result.json
assert '{"score": 4}' == result.json
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -685,7 +685,7 @@ def test_increment_tool_errors():
with patch.object(Task, "increment_tools_errors") as increment_tools_errors:
increment_tools_errors.return_value = None
crew.kickoff()
assert len(increment_tools_errors.mock_calls) == 9
assert len(increment_tools_errors.mock_calls) == 18
def test_task_definition_based_on_dict():