Courtesy of Steve Blank (Godfather of Lean Startup, Mentor to your editor and designer of Customer Development evidenced-based entrepreneurship).
Hundreds of billions in public and private capital is being invested in Artificial Intelligence (AI) and Machine Learning companies. The number of patents filed in 2021 is more than 30 times higher than in 2015 as companies and countries across the world have realized that AI and Machine Learning will be a major disruptor and potentially change the balance of military power.
Until recently, the hype exceeded reality. Today, however, advances in AI in several important areas (here, here, here, here and here) equal and even surpass human capabilities. If you haven’t paid attention, now’s the time.
Artificial Intelligence and the Department of Defense (DoD)
The Department of Defense has thought that Artificial Intelligence is such a foundational set of technologies that they started a dedicated organization- the JAIC – to enable and implement artificial intelligence across the Department. They provide the infrastructure, tools, and technical expertise for DoD users to successfully build and deploy their AI-accelerated projects.
We’re in the Middle of a Revolution
Imagine it’s 1950, and you’re a visitor who traveled back in time from today. Your job is to explain the impact computers will have on business, defense and society to people who are using manual calculators and slide rules. You succeed in convincing one company and a government to adopt computers and learn to code much faster than their competitors /adversaries. And they figure out how they could digitally enable their business – supply chain, customer interactions, etc. Think about the competitive edge they’d have by today in business or as a nation. They’d steamroll everyone.
That’s where we are today with Artificial Intelligence and Machine Learning. These technologies will transform businesses and government agencies. Today, 100s of billions of dollars in private capital have been invested in 1,000s of AI startups. The U.S. Department of Defense has created a dedicated organization to ensure its deployment.
But What Is It?
Compared to the classic computing we’ve had for the last 75 years, AI has led to new types of applications, e.g. facial recognition; new types of algorithms, e.g. machine learning; new types of computer architectures, e.g. neural nets; new hardware, e.g. GPUs; new types of software developers, e.g. data scientists; all under the overarching theme of artificial intelligence. The sum of these feels like buzzword bingo. But they herald a sea change in what computers are capable of doing, how they do it, and what hardware and software is needed to do it. This brief will attempt to describe all of it.
New Words to Define Old Things
One of the reasons the world of AI/ML is confusing is that it’s created its own language and vocabulary. It uses new words to define programming steps, job descriptions, development tools, etc. But once you understand how the new world maps onto the classic computing world, it starts to make sense. So first a short list of some key definitions.
AI/ML – a shorthand for Artificial Intelligence/Machine Learning
Artificial Intelligence (AI) – a catchall term used to describe “Intelligent machines” which can solve problems, make/suggest decisions and perform tasks that have traditionally required humans to do. AI is not a single thing, but a constellation of different technologies.
Machine Learning (ML) – a subfield of artificial intelligence. Humans combine data with algorithms (see here for a list) to train a model using that data. This trained model can then make predications on new data (is this picture a cat, a dog or a person?) or decision-making processes (like understanding text and images) without being explicitly programmed to do so.
Machine learning algorithms – computer programs that adjust themselves to perform better as they are exposed to more data. The “learning” part of machine learning means these programs change how they process data over time. In other words, a machine-learning algorithm can adjust its own settings, given feedback on its previous performance in making predictions about a collection of data (images, text, etc.).
Deep Learning/Neural Nets – a subfield of machine learning. Neural networks make up the backbone of deep learning. (The “deep” in deep learning refers to the depth of layers in a neural network.) Neural nets are effective at a variety of tasks (e.g., image classification, speech recognition).rning neural net algorithm is given massive volumes of data, and a task to perform – such as classification. The resulting model is capable of solving complex tasks such as recognizing objects within an image and translating speech in real time. In reality, the neural net is a logical concept that gets mapped onto a physical set of specialized processors. See here.)
Data Science – a new field of computer science. Broadly it encompasses data systems and processes aimed at maintaining data sets and deriving meaning out of them. In the context of AI, it’s the practice of people who are doing machine learning.
Data Scientists – responsible for extracting insights that help businesses make decisions. They explore and analyze data using machine learning platforms to create models about customers, processes, risks, or whatever they’re trying to predict.
What’s Different? Why is Machine Learning Possible Now?
To understand why AI/Machine Learning can do these things, let’s compare them to computers before AI came on the scene. (Warning – simplified examples below.).
In contrast to programming on classic computing with fixed rules, machine learning is just like it sounds – we can train/teach a computer to “learn by example” by feeding it lots and lots of examples. (For images a rule of thumb is that a machine learning algorithm needs at least 5,000 labeled examples of each category in order to produce an AI model with decent performance.) Once it is trained, the computer runs on its own and can make predictions and/or complex decisions.
Just as traditional programming has three steps – first coding a program, next compiling it and then running it – machine learning also has three steps: training (teaching), pruning and inference (predicting by itself.)
Machine Learning – Training
Unlike programing classic computers with explicit rules, training is the process of “teaching” a computer to perform a task e.g. recognize faces, signals, understand text, etc. (Now you know why you’re asked to click on images of traffic lights, cross walks, stop signs, and buses or type the text of scanned image in ReCaptcha.) Humans provide massive volumes of “training data” (the more data, the better the model’s performance) and select the appropriate algorithm to find the best optimized outcome. (See the detailed “machine learning pipeline” section for the gory details.)
What Does this Mean for Businesses?
Hang on to your seat. We’re just at the beginning of the revolution. The next phase of AI, powered by ever increasing powerful AI hardware and cloud clusters, will combine some of these basic algorithms into applications that do things no human can. It will transform business and defense in ways that will create new applications and opportunities.
Applications with embedded intelligence have already begun to appear thanks to massive language models. For example – Copilot as a pair-programmer in Microsoft Visual Studio VSCode. It’s not hard to imagine DALL-E 2 as an illustration assistant in a photo editing application, or GPT-3 as a writing assistant in Google Docs.
AI in Medicine
AI applications are already appearing in radiology, dermatology, and oncology. Examples: IDx-DR,OsteoDetect, Embrace2. AI Medical image identification can automatically detect lesions, and tumors with diagnostics equal to or greater than humans. For Pharma, AI will power drug discovery design for finding new drug candidates. The FDA has a plan for approving AI software here and a list of AI-enabled medical devices here.
Harder than it first seemed, but car companies like Tesla will eventually get better than human autonomy for highway driving and eventually city streets.
Advanced virtual assistants can listen to and observe behaviors, build and maintain data models, and predict and recommend actions to assist people with and automate tasks that were previously only possible for humans to accomplish.
Supply chain management
AI applications are already appearing in predictive maintenance, risk management, procurement, order fulfillment, supply chain planning and promotion management.
AI applications are already appearing in real-time personalization, content and media optimization and campaign orchestration to augment, streamline and automate marketing processes and tasks constrained by human costs and capability, and to uncover new customer insights and accelerate deployment at scale.
Making business smarter: Customer Support
AI applications are already appearing in virtual customer assistants with speech recognition, sentiment analysis, automated/augmented quality assurance and other technologies providing customers with 24/7 self- and assisted-service options across channels.