Categories
innovation technology

GTP3 – Will AI replace programmers?

Huge thank you to those who checked out my last video, I was taken aback by the reaction and it was awesome to interact with so many people from around the world.

Reactions tended to break down into two categories.

  • This is so cool
  • OMG – my job! Will AI replace programmers?

I want to show some additional GPT3 demos and I also explain some of the different types of AI, machine learning, and how it’s amazing and cool but you don’t have to worry about your job, at least not YET! We do however need to worry about AI having bias and being racist, sexist, and more.

Let’s start with some recent highlights…

A context aware dictionary that knows the definition of a word based on the context.
An example of image recognition paired with GPT3 to show good and bad ingredients in a product.

An example of using GPT3 as a function within a spreadsheet.
An example of CSS and layout generation using GPT3
https://thoughts.sushant-kumar.com/. An example of a quote generator based on GPT3
An example of UI generation within Figma using GPT3
An example of using GPT3 to write SQL queries

These demos are amazing progress but in order to unsertand why programmer jobs aren’t in gepordy you need to understand the basics of AI. There are four categories to consider:

  • 1. Reactive machines – Big Blue / Chess-playing – they look at the environment and react.
  • 2. Machines with memory – These are AI’s that can remember things over time and learn from their environment. Self-driving cars are an example of this.
  • 3. Theory of mind – This is when a machine can think and understand that others can think. It can put itself in the shoes of someone else and serve basic needs and functions in a general way. This is called Artificial General Intelligence
  • 4. Self-aware – This is when a machine can has the abilities of the previous categories and can also understand it’s own existance. This is in the realm of science fiction and both categories #3 & 4 are theoretical areas of research and we’re not close to these yet.

GPT3 is mostly #1. While it has a lot of data it’s not designed to remember things from session to session. The model is pre-trained, that’s the PT in GPT and you can think of it as the world’s most sophisticated auto-complete. Similar to how when you start typing and Google completes the sentence. GPT is able to complete questions, code, html files, and more. Because it’s trained on so much data the auto-complete has context but not memory. It’s incredibly good but it’s not perfect and it isn’t tested as valid.

Most of the time the output of GPT3 will be a starting point, not the final product. In the examples above the HTML, SQL, CSS, and text that is produced is most likely to be a starting point but its quality and fidelity, while impressive is unlikely to be a final result.

As I said GPT3 is an amazing piece of technology and I can understand why people may worry about their job. Technology can cause this concern and it’s been going on since Aristotle and Ancient Greeks. Farmers have worried about tractors. Scribes worried about the printing press and mathematicians and typists worried about computers. There’s a term for this is Technological unemployment.

While technology can eliminate or shift jobs it also tends to create new jobs and new opportunities. Even if GPT3 is really good, the world will still need engineers, designers, poets, and creators, perhaps more than ever.

The problem with AI

I tend to be an optimist but there are areas that still need a lot of work when it comes to AI and in particular, bias tends to be a real problem.

Here Chukwuemeka shows an image recognition that isn’t trained with diversity in mind…

This is why diversity in technology is so important and it’s also why we need to be careful about the data that’s driving and powering the worlds most powerful autocomplete. AI tends to work off of large collections of data. This can be imaging data, text data. If we’re not careful about the input data and testing it can produce problems.

In another study out of MIT Joy Buolamwini explores the notion of algorithmic bias and how it can be a huge problem.

Joy has a great TedX talk on this topic if you want to learn more.

As developers start incorporating GPT3 into their products and technologies, it’s important that they consider all sorts of biases that may be in the data.

Bad jokes, offensive ideas, historical inaccuracies, propaganda, sexism, racism, and more. In the billions of tokens that GPT3 has processed, it’s gotten good at auto-completing many things including some that we may offensive, inaccurate, or even dangerous.

Sam Altman – one of the founders of OpenAI touched on this recently in response to Jerome Pesenti, the head of AI at Facebook:

It’s great that OpenAI is taking bias seriously and it’s important that engineers building and incorporating AI into their products consider how their training data may have biases.

Thank you to everyone who watched my last video and checked out this post. I’m incredibly grateful to you for your feedback and comments. If you’re new to the blog, I tend to talk about entrepreneurship, technology, design, so if you like that sort of thing you can sign-up to get updates when I post. You can also subscribe on YouTube if you prefer.

Categories
innovation technology

GPT 3 Demo and Explanation

Last week GPT3 was released by OpenAI and it’s nothing short of groundbreaking. It’s the largest leap in artificial intelligence we’ve seen in a long time and the implications of these advances will be felt for a really long time.

GPT 3 can write poetry, translate text, chat convincingly, and answer abstract questions. It’s being used to code, design, and much more.

I’m going to give you the basics and background of GPT3 and show you some of the amazing creations that have started to circle the Internet in just the first week of the technology being available to a limited set of developers.

Let’s start with a few examples of what’s possible.

Demonstration of GPT-3 designing user interface components:

The designer is able to describe the interface that they want and the GPT3 plug-in to Figma is able to generate the UI.

GPT3 creating a simple react application:

Here the developer describes the React application that they want and the AI writes a function with the hooks and events needed to function correctly.
A couple of examples of GPT3 creating paragraphs of text based on initial cues of what is needed.
In this example GPT3 is able to complete an Excel table of data.
This example demonstrates a web plug-in to find answers within a Wikipedia article.
Example of using GPT3 as an answer engine for arbitrary questions.

Background

GTP3 comes from a company called OpenAI. OpenAI was founded by Elon Musk and Sam Altman (former president of Y-combinator the startup accelerator). OpenAI was founded with over a Billion invested to collaborate and create human-level AI for the benefit of the human race.

OpenAI has been developing it’s technology for a number of years. One of the early papers published was on Generative Pre-Training. The idea behind generative pre-training is that while most AI’s are trained on labeled data, there’s a ton of data that isn’t labeled. If you can evaluate the words and use them to train and tune the AI it can start to create predictions of future text on the unlabeled data. You repeat the process until predictions start to converge.

The original GPT stands for Generative Pre Training and the original GPT used 7000 books as the basis of training. The new GPT3 is trained on a lot more… In fact it’s trained on 410 billion tokens from crawling the Internet. 67 Billion from books. 3 Billion from Wikipedia and much more. In total it’s 175 Billion parameters and 570GB of filtered text (over 45 Terrabytes of unfiltered text)

Over an ExaFLOP day of compute needed to train the full data set.

The amount of computing power that was used to pre-train the model is astounding. It’s over an exaflop day of computer power. One second of exaflop computer power would allow you to run a calculation per second for over 37 Trillion years.

The GPT3 technology is currently in limited Beta and early access developers are just starting to produce demonstrations of the technology. As the limited Beta expands you can expect to see a lot more interesting and deep applications of the technology. I believe it’ll shape the future of the Internet and how we use software and technology.

Links:

Categories
innovation startups

Ideas Are Worthless

You may thinkyou have the best, most amazing idea but I’m sorry to tell you that your idea is worthless…. But it’s Ok, most ideas are worthless.

Now before I get too deep, I’ve seen hundreds of pitches with a wide range of ideas and I’ve signed stacks and stacks of NDA’s to keep someone’s ideas secrets. Want to know the best secret idea I’ve ever heard?

There are none. We’re you listening at the beginning? Ideas are worthless and I’ve never been blown away by an amazing idea. Never! I’ve heard interesting ideas and clever ideas but most of the time amazing ideas are not the exciting part.

If you just think about the ideas behind the world’s most successful companies, the ideas aren’t that exciting.

  • A phone that doesn’t have any buttons
  • A car that uses electricity instead of a motor
  • A new search engine

These ideas by themselves have no value and even if you were able to rewind the clock 20 years, the ideas themselves weren’t worth anything without the entrepenours to drive them.

Nokia had phones without buttons before Apple. There were plenty of electric golf-carts before Tesla, and Google was late to the game as far as search engines go.

It’s the execution that creates value and these companies executed exceptionaly well.

While ideas are worthless, working on your idea is the thing that starts to create value. Some examples of value creation:

  • A list of potential customers willing to try or buy a finished product
  • Sales or purchase orders for a product or service
  • A prototype of the future product
  • Testimonials from people who have tried the prototype/product
  • Partners willing to stock or sell the product/service
  • Patents on the product/technology. (more on patents here)

You don’t have to be an engineer or designer to make progress on an idea, but you need to take action.

The other reason that ideas are worthless is that the idea instantly changes as soon as you start working on it. Once you put a pencil to paper your idea starts to spawn new ideas. Once you have a customer using the product you start to get feedback on the idea and what needs to change about it. Once you try to sell a product you learn all the reasons people don’t want it. It’s this learning/feedback cycle that creates real value because it’s based on real applications, not just theoretical ones.

The execution of the idea is the essence of the idea. Want to make something amazing, take action to make it real.

Why are ideas worthless
Categories
innovation startups

Angel investors vs Venture Capitalists

Angel vs. Venture Capitalists.

Startups looking to raise money may not be too picky in terms of where they get it, but finding the right fit for your company is often more than just financing and may reflect some of the differences between angel investors and venture capitalists.

First let’s start off with where Angel Investors and Venture Capital Investors get their money.

  • Angel Investors are typically individuals – They typically don’t have other decision makers in their investments and they are usually investing their own money. This gives them flexibility in terms of deal terms and it also means that they often don’t have external requirements on how they get their money back.
  • Venture Capital investors are typically not individuals, but rather companies or firms. They are most typically investing other people’s money in a Fund. VC’s will raise this money from people referred to as Limited Partners or LPs. LP’s are typically writing million-dollar checks and expecting VC’s to invest that money and get a return.

Both Angels and Venture Capitalists look for companies that can grow and be successful but each may look at companies at different stages and be interested in making different types of investments. Because VC’s are investing other people’s money they have general expectations on how long it may take to get their money back and will structure most returns and investments to have liquidity.

Angel investments are typically investing in early stage companies and are most often writing checks between 5-50 thousand with some angel investors going even higher. It’s most typical for angels to invest in the early stages of growth.

Angels and VC’s may take different amounts of interest in the operations of the company too. While Angels will often be available and interested in helping companies VC’s are likely to insist on a board seat. As companies continue to raise funding founders should be aware of balance in the board of directors.

Generally the board and the founder are aligned however if the founder and board disagree it’s possible for the board to fire the CEO so just make sure you consider the long-term direction of your business as you take on investors and board members.

Ultimately both VC’s and Angels want companies to succeed and depending on the stage of your business angels or VC’s may be a better fit for growing your business. Lastly remember that you don’t have to take investment and there are plenty of successful companies that have never raised funding and did it all on their own. There’s no right or wrong so consider the pros and cons of the different paths as you go on your startup journey.