Your Comprehensive Guide to the AI Landscape

Machine learning, Applied AI, Deep Learning, Generative AI, Narrow AI, AGI

Hello conscious designers! I hope you enjoyed our previous substack on circular design. ♻️ If you missed it, you can also listen to the podcast here. 🎤

Someone asked me at a conference “what is the most overhyped thing right now?” and my response was AI. 😂

Don’t get me wrong, it’s here to stay. I responded this way because we were already using AI but because of the hype, sometimes teams come out with a new AI feature that isn’t actually necessary. Nevertheless, as designers we need to be at the forefront of all technology so let’s talk about what actually matters. Because there’s SO much to cover, I’ll spare your brain 🧠 and make this a series of articles rather than an essay. 😉

Thijs Kraan (previous pod guest) and I at TNW Conference

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Btw, here’s how I’d like to help you:

• Get the Sustainable Growth Playbook - Learn to design with a business lens

• Book a 1-1 Call with Me - Guidance for Founders and Designers

• Join The Craft’s Discord - We’re reading Design After Capitalism. 📚

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Alright, let’s get into AI. 🤖 Today we’re going to cover:

• Understand AI for Technologists

• The History of AI

• All the fancy terms you need to know

• Courses to learn more

Understanding AI for Technologists

Artificial intelligence (AI) is a machine’s ability to perform cognitive functions, such as speech recognition, decision making, and identifying patterns. AI is an umbrella term for a wide variety of technologies, including machine learning, deep learning, and natural language processing (NLP).

AI is trained on huge amounts of information in order to learn to identify patterns. By doing so, it can carry out tasks such as human-like conversation, analyzing medical images, preventing fraud, or generating content. You may be familiar with AI being used for voice assistants, chatbots, and even predicting the weather! ☀️

Here are some examples of how companies use AI:

1. Netflix Profiles: Netflix uses AI algorithms to analyze viewing behaviours and preferences to recommend personalized content to users. This helps increase viewer engagement and satisfaction.

2. Google DeepMind + UK Energy: Google collaborated with the UK’s National Grid to use AI for predicting energy demand and optimizing energy distribution. It can also be used for integrating renewable energy sources and preventing blackouts.

3. Tesla Autonomous Driving: Tesla's Autopilot and Full Self-Driving (FSD) features rely heavily on AI to process data from cameras, radar, and ultrasonic sensors to navigate and drive autonomously.

4. Apple Health Monitoring: AI powers health features in Apple Watch, such as heart rate monitoring, fall detection, and activity tracking, providing users with personalized health insights.

5. Fedex Logistics: AI helps optimize logistics, such as delivery routes, reducing fuel consumption and delivery times.

   

The Development of AI As We Know It

In 1950, Alan Turing publishes "Computing Machinery and Intelligence," proposing the Turing Test to determine if a machine can exhibit intelligent behavior indistinguishable from a human. This is when he introduced the concept “imitation game,” which is now a movie with Benedict Cumberbatch and you can watch it on Netflix for homework. 😜

In 1956, the term artificial intelligence came into fruition, coined by computer scientists, John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon for a workshop at Dartmouth. In 1965, Joseph Weizenbaum develops ELIZA, an early natural language processing computer program that simulates conversation.

In 1969, Marvin Minsky and Seymour Papert publish "Perceptrons," a book that explores the limitations of neural networks. Neural networks are the technology behind the explosive growth of generative AI. This models the ways neurons interact in the human brain, neural networks take in data and process it through multiple iterations that learn increasingly complex features of the data.

Emerging from a period in the 80s, known as the “AI winter,” in 1997, IBM's Deep Blue defeats world chess champion Garry Kasparov, marking a significant achievement in AI. In 2006, Geoffrey Hinton, Yoshua Bengio, and Yann LeCun's work on deep learning and neural networks revitalizes AI research, leading to significant advancements in computer vision. In 2016, Google DeepMind's AlphaGo defeats world champion Go player Lee Sedol, showcasing the power of deep reinforcement learning.

AI Terminology to Know

Applied AI

Applying AI to real-world applications to solve specific problems across various industries. Companies have the potential to make business more efficient and profitable. For example, Google Maps uses applied AI to provide optimal routes and predict travel times by analyzing data from various sources including user location, historical traffic data, and live traffic conditions.

Artificial General Intelligence

AGI is a term used to describe AI system that could possess capabilities comparable to those of a human’s cognitive abilities, such as reasoning, problem solving, creativity and more. Academics believe we are decades away from AGI. Rodney Brooks, an MIT roboticist and cofounder of iRobot, doesn’t believe AGI will arrive until the year 2300.¹