Summary - You Look Like A Thing And I Love You

This is my favorite book on AI so far. Concise, funny, realistic. Every PM should read it to understand what (and what not) AI can do in the next years in product. You should read "You Look Like a Thing and I Love You" by Janelle Shane.

And here's my summary, chapter by chapter.

Introduction: AI is everywhere

• AI learns by analyzing examples and finding patterns to solve problems
• AI is present in many aspects of modern life
• AI is not perfect and can make mistakes
• The inner workings of AI can be complex and hard to understand
• The potential danger of AI is its lack of intelligence rather than excess of it
• AI has more limited brainpower compared to humans
• AI may not fully understand the problem it is trying to solve
• AI will try to do what it is told, but may not always succeed
• AI will choose the easiest solution to a problem

Chapter 1: What is AI?

• AI is a type of machine learning algorithm
• Machine learning algorithms figure out how to solve a problem through trial and error
• AI is trained like a child, not programmed like a traditional computer
• AI can be used to solve many different types of problems
• Some AI systems use a combination of human and machine control
• AIs may make mistakes that are hard to detect
• Studying an AI's internal structure can be complex
• AIs may imitate flawed human decision-making
• It can be difficult to identify good candidates for a job
• A problem may not be what it initially seems
• AIs may use biased or flawed data to make decisions
• AIs may try to learn from flawed data, leading to flawed decision-making

Chapter 2: AI is everywhere, but where is it exactly?

• AI is used to analyze large datasets and find trends
• Major news outlets use AI to write articles
• AI can perform tasks more consistently than humans
• AI is currently limited to narrow, specialized tasks
• AI needs large amounts of data to learn effectively
• AI can improve and learn faster with transfer learning
• AI has a limited memory and struggles with tasks that require long-term planning or dependence on previous information
• AI can generate text, but may struggle with long-term dependencies or understanding context
• Some AI systems can identify patterns in data and make predictions, but may struggle with understanding the underlying causes of those patterns
• AI may struggle with tasks that require understanding and adapting to changing situations or environments
• Some AI systems can be trained to recognize and classify objects in images, but may struggle with understanding and adapting to changes in those objects or their surroundings
• AI may struggle with tasks that require understanding and adapting to the emotions or intentions of other beings

Chapter 3: How does it actually learn?

• Artificial neural networks (ANNs) are a common type of AI
• ANNs are inspired by the way the human brain works and are made up of simple software units that perform simple math
• The complexity of ANNs comes from how these units are connected
• ANNs can have many layers, a process called deep learning
• ANNs need to be trained with data to perform a task
• Class imbalance, where one category of data is much more prevalent than others, can cause problems for ANNs
• The rules used by ANNs to solve a problem may be hard to understand
• It is easier to understand the functions of cells in image-recognizing or image-generating ANNs
• Markov chains and random forest algorithms are also used in machine learning
• Evolutionary algorithms can be used to train AI by simulating evolution and testing different solutions
• Tweaking certain options, called hyperparameters, is important in building evolutionary algorithms
• AI can be used to solve problems that are too complex or time-consuming for humans
• It can be difficult to find the best solution to a complex problem using AI
• Human interpretation and understanding of an AI's solution to a problem is important

Chapter 4: It’s trying!

• AI is often used to solve problems and can learn from examples
• AI is used in various fields and is not perfect
• AI algorithms can be difficult to understand and their outputs can reveal mistakes
• AI's danger is not that it is too smart, but that it is not smart enough
• AI does not fully understand the problems it is asked to solve and follows instructions exactly
• AI follows the path of least resistance
• AI needs a lot of data and specific tasks to be effective
• AI can be trained with video games and other simulations
• Crowdsourcing and data augmentation can improve AI performance
• Labeling errors in training data can lead to AI mistakes
• AI can be biased by the data it is trained on and can have security risks
• AI can be unprepared for unusual situations.

Chapter 5: What are you really asking for?

• AIs are prone to solving the wrong problem because they develop their own ways of solving problems and lack contextual knowledge
• Training a machine learning algorithm is similar to training a dog
• Overfitting, or preparing for specific conditions that don't match the real world, can be a problem
• AIs can only learn one narrow task at a time
• It's important for machine learning programmers to specify the problem and reward function for the algorithm
• The noisy TV problem, where an AI seeks chaos instead of true curiosity, can occur
• The effect of certain videos on YouTube can lead to people watching more of them even if the effect of watching them is negative.

Chapter 6: Hacking the Matrix, or AI finds a way

• Simulated organisms can evolve to find energy sources
• No organism has learned to exploit reality glitches
• Virtual experiences lack real-world understanding and can be unsafe

Chapter 7: Unfortunate shortcuts

• AI can be trained with imbalanced data by rewarding rare finds
• AI may overfit if trained on too few examples
• AI can learn to predict human behavior
• AI can predict where crime will be detected, not where it will occur
• Expect AI to copy human biases and consider bias screening or certification
• Treating AI decisions as impartial is called "mathwashing" or "bias laundering"

Chapter 8: Is an AI brain like a human brain?

• Many AI characteristics and behaviors mirror those of living organisms
• Quick reflexes often rely on internal models to predict best reaction
• Some neuroscientists believe dreaming allows for low-stakes training with internal models
• Catastrophic forgetting is a limitation of neural networks and long-term memory
• Catastrophic forgetting is a major obstacle to creating human-level AI
• Future AI may resemble swarms of social insects more than humans
• Adversarial attacks can fool image recognition algorithms
• Limited number of large, free image datasets available for AI training
• Narrower AI tasks may result in seeming intelligence

Chapter 9: Human bots (where can you not expect to see AI?)

• Many European startups classified as AI do not actually use AI
• Companies may claim to have AI capabilities they cannot demonstrate
• Customers may be unaware that human employees are handling sensitive information
• Misconceptions about AI capabilities may arise if claims of human-like performance are made
• Some may try to deceive about using advanced AI for surveillance
• To evaluate the effectiveness of AI, consider the source and breadth of training data, memory requirements, and potential for bias
• Complex tasks such as understanding human language and evaluating individuals may be difficult for AI and may result in the use of harmful biases.

Chapter 10: A human-AI partnership

• Future AI use is likely to involve collaboration with humans to solve problems and automate tasks
• Practical machine learning combines rules-based programming with open-ended machine learning
• It is possible for humans to understand problems so well that machine learning is no longer needed
• AI can be used to flag offensive content, but may also incorrectly flag discussions of offensive content
• AI can be used to facilitate collusion and potentially illegal actions by sellers
• Humans may serve as editors in AI processes
• AI can be more consistent than humans, but may struggle with unexpected situations.

Conclusion: Life among our artificial friends

• The immediate danger of AI is not its intelligence, but rather its lack thereof
• AI can only understand what it has been trained to recognize and process
• The complexity and unpredictability of the world may be beyond the scope of current AI capabilities.