Google’s AI Course for Beginners (in 10 minutes)!

if you don't have a technical background but you still want to learn the basics of artificial intelligence stick around because we were distilling Google's 4-Hour AI course for beginners into just 10 minutes I was initially very skeptical because I thought the course would be too conceptual we're all about practical tips on this channel and knowing Google the course might just disappear after 1 hour but I found the underlying Concepts actually made me better at using tools like Chachi BT and Google bard and cleared up a bunch of misconceptions I didn't know I had about AI

machine learning and large language models so starting with the broadest possible question what is artificial intelligence it turns out and I'm so embarrassed to admit I didn't know this AI is an entire field of study like physics and machine learning is a subfield of AI much like how thermodynamics is a subfield of physics going down another level deep learning is a subset of machine learning and deep learning models can be further broken down into something called discriminative models and generative models large language models llms also fall under deep learning and right at the intersection between

generative and llms is the technology that powers the applications we're all familiar with chat gbt and Google bard let me know in the comments if this was news to you as well now that we have an understanding of the overall landscape and you see how the different disciplines sit in relation to each other let's go over the key takeaways you should know for each level in a nutshell machine learning is a program that uses input data to train a model that train model can then make predictions Based on data it has never seen before for

example if you train a model based on Nike sales data you can then use that model to predict how well a new shoe from Adidas would sell based on Adidas sales data two of the most common types of machine learning models are supervised and unsupervised learning models the key difference between the two is supervised models use labeled data and unsupervised models use unlabeled data in this supervised example we have historical data points that plot the total bill amount at a restaurant against the tip amount and here the data is labeled Blue Dot equals the order

was picked up and yellow dot equals the order was delivered using a supervised learning model we can now predict how much tip we can expect for the next order given the bill amount and whether it's picked up or delivered for unsupervised learning models we look at the raw data and see if a naturally falls into groups in this example we plotted the employee tenure at a company against their income we see this group of employees have a relatively High income to years work ratio versus this group we can also see all these are unlabeled data

if they were labeled we would see male female years worked company function Etc we can now ask this unsupervised learning model to solve a problem like if a new employee joins are they on the FasTrack or not if they appear on the left then yes if they appear on the right then no Pro tip another big difference between the two models is that after a supervised learning model makes a prediction it will compare that prediction to the training data used to train that model and if there's a difference it tries to close that Gap unsupervised

learning models do not do this by the way this video is not sponsored but it is supported by those of you who subscribe to my paid productivity newsletter on Google tips Link in the description if you want to learn more now we have a basic grasp of machine learning it's a good time to talk about deep learning which is just a type of machine learning that uses something called artificial neural networks don't worry all you have to know for now is that artificial neural networks are inspired by the human brain and looks something like this

layers of nodes and neurons and the more layers there are the more powerful the model and because we have these neural networks we can now do something called semisupervised learning whereby a deep learning model is trained on a small amount of labeled data and a large amount of unlabeled data for example a bank might use deep learning models to detect fraud the bank spends a bit of time to tag or label 5% of transactions as either fraudulent or not fraudulent and they leave the remaining 95% of transactions unlabeled because they don't have the time or

resources to label every transaction the magic happens when the Deep learning model uses the 5% of label data to learn the basic concepts of the task okay these transactions are good these are bad okay apply those learnings to the remaining 95% of unlabeled data and using this new aggregate data set the model makes predictions for future transactions that's pretty cool and we're not done because deep learning can be divided into two types discriminative and generative models discriminative models learn from the relationship between labels of data points and only has the ability to classify those data

points fraud not fraud for example you have a bunch of of pictures or data points you purposefully label some of them as cats and some of them as dogs a discriminative model will learn from the label cat or dog and if you submit a picture of a dog it will predict the label for that new data point a dog we finally get to generative AI unlike discriminative models generative models learn about the patterns in the training data then after they receive some input for example a text prompt from us they generate something new based on

the patterns they just learned going back to the animal example the pictures or data points are not labeled as cater doog so a generative model will look for patterns oh these data points all have two ears four legs a tail likes dog food and Barks when as to generate something called a dog the generative model generates a completely new image based on the patterns it just learned there's a super simple way to determine if something is generative AI or not if the output is a number a classification spam not spam or a probability it is

not generative AI it is Gen AI when the output is natural language text or a speech an image or audio basically generative AI generates new samples that are similar to the data it was trained on moving on to different generative AI model types most of us are familiar with textto text models like Chach BT and Google bard other common model types include text to image models like mid Journey Dolly and stable diffusion these can not only generate images but edit images as well text to video models surprise surprise can generate and edit video footage examples

include Google's imageen video Cog video and the Very creatively named make a video text to 3D models are used to create game assets and a little known example would be open ai's shape e model and finally text to task models are trained to perform a specific task for example if you type at gmail summarize my unread emails Google bard will look through your inbox and summarize your unread emails moving over to large language models don't forget that llms are also a subset of deep learning and although there is some overlap llms and geni are not

the same thing an important distinction is that large language models are generally pre-trained with a very large set of data and then fine-tune for specific purposes what does that mean imagine you have a pet dog it can be pre-trained with basic commands like sit come down and stay it's a good boy and a generalist but if that same good boy goes on to become a police dog a guide dog or hunting dog they need to receive specific training so they're fine tuned for that specialist role a similar idea applies to large language models they're first

pre-trained to solve common language problems like text classification question answering document summarization and text generation then using smaller industry specific data sets these llms are fine-tuned to solve specific problems in Retail Finance healthare entertainment and other fields in the real world this might mean a hospital uses a pre-trained large language model from one of the big tech companies and fine-tunes that model with its own first-party medical data to improve diagnostic accuracy from X-rays and other medical tests this is a win-win scenario because large companies can spend billions developing general purpose large language models then sell

those llms to smaller institutions like retail companies Banks hospitals who don't have the resources to develop their own large language models but they have the domain specific data sets to fine-tune those models Pro tip if you do end up taking the full course I'll link it down below it's completely free when you're taking notes you can right click on the video player and copy video URL at the current time so can quickly navigate back to that specific part of the video there are five modules total and you get a badge after completing each module the

content overall is a bit more on the theoretical side so you definitely want to check out this video on how to master prompting next see you on the next video in the meantime have a great one