On 29th May 2019, I completed an Artificial Intelligence course by Andrew Ng. Andrew Ng is Co-founder of Coursera, an and Adjunct Professor of Computer Science at Stanford University.
Why did I do this course:
I wanted to learn these because AI has been the buzzword and is changing the way we work and live. As a PM, AI Product Manager now has to need new skill sets to figure out what’s feasible and valuable in light of what AI can and cannot do today.
I wanted to learn about the meaning behind AI terminology, including neural networks, machine learning, deep learning, and data science. What AI realistically can — and cannot — do. How to spot opportunities to apply AI to business problems. What it feels like to build machine learning and data science projects. How to work with an AI team and build an AI strategy in a company.
So here is my learnings in bullet points:
· What Machine Learning Can and Cannot do- One imperfect rule of thumb you can use to decide what supervised learning may or may not be able to do is that, pretty much anything you could do with a second of thought, we can probably now or soon automate using supervised learning, using this input-output mapping.
· The key steps of a machine learning project, which are to collect data, to train the model, and then to deploy the model.
· The key steps of a data science project are to collect the data, to analyze the data, and then to suggest hypotheses and actions.
· How to choose an AI project: Technical diligence is the process of making sure that the AI system you hope to build really is doable, really is feasible. So, you might talk to AI experts about whether or not the AI system can actually meet the desired level of performance. A second important question for technical diligence is how much data is needed to get to this desired level of performance, and do you have a way to get that much data. Third, would be an engineering timeline to try to figure out how long it will take and how many people it will take to build a system that you would like to have built. In addition to technical diligence, conduct business diligence to make sure that the project you envision really is valuable for the business.
· What are the data sets required to train an ML algorithm? training set what a machine learning will do is learn in other words computer or figure out some mapping from A to B so that you now have a piece of software that can take as input the input A and try to figure out what is the appropriate output B. So, the training set is the input to the machine learning software that lets it figure out what is this A to B mapping.
1. The second dataset that an AI team will use is the test set. As you’ve seen this is just another set of images that’s different from the training sets also with the labels provided. The way an AI team will evaluate their learning algorithms performance is to give the images into the test set to the AI software and see what the AI software outputs.
· Problems in data: Biased, Insufficient, Mislabeled
· For an AI/ML project you must consider CPU and GPU power because to process large neural network, we need large processing power. Also, consider cloud vs on-premise vs edge.
· Case studies to learnt: Smart Speakers, Self Driving Cars, and Resume Screening.
· Roles of an AI team:
1. Software Engineers for engineering tasks — over 50% of the team comprise of these engineers.
2. Machine Learning Engineer — write the software responsible for generating the A to B mapping or for building other machine learning algorithms needed for your product.
3. Machine Learning Researcher — is to extend the state of the art in machine learning. Finding ways to adapt them to the problem they are facing such as how to take the most cutting edge, trigger where the wicker detection algorithm
4. Data Scientists is to examine data and provide insights, as well as to make presentations to teams or the executives in order to help drive business decision-making.
5. Data Engineers -whose main role is to help you organize your data, meaning, to make sure that your data is saved and is easily accessible, secure and cost-effective way.
6. AI Product Managers whose job is to help decide what to build. They help to figure out what’s feasible and valuable. Traditional Product Manager’s job was already to decide what to build as well as sometimes some other roles, but the AI Product Manager now has to do this in the AI era and they’re needing new skill sets to figure out what’s feasible and valuable in light of what AI can and cannot do today.
· AI transformation steps for a company:
1. Step one is for your company to execute pilot projects to gain momentum. Start to know what it feels like to work on AI projects.
2. Step two, is to build an in-house AI team.
3. Step three, is to provide broad AI training, not just the engineers but to many levels within a company including executives.
4. Step four, is to develop your AI strategy and
5. step five, is to develop internal and external communications about your company and AI.
· It may be more efficient to take the AI talent in your centralized AI unit and matrix them into the gift card business unit so that your AI talent can work together with the gift card domain experts in order to develop interesting AI projects together. AI unit is to build a company wide platforms. A single business unit may not have the resources or the incentive to build these company-wide platforms and resources that can support the whole company but essentialized AI team maybe help built these company wide tools or platforms that can help multiple business units.
· The marketplace model requires AI to reduce costs, increase leads, sales, and automate the manual stuff. Pitfalls to avoid for AI transformation.
1. AI can’t solve everything. Know what AI can technically solve and how does it really have a business impact.
2. Don’t just hire two or three machine learning engineers and count solely on them to come up with use cases for your company. Machine learning engineers are a scarce resource but you should instead air the engineer talents with business talent and work cross-functionally to find feasible and valuable projects.
3. Don’t expect AI project to work the first time. AI development is often an iterative process so should plan for it through an iterative process with multiple attempts needed to succeed.
4. Don’t expect traditional planning processes to apply without changes. Instead, you should work with the AI team to establish timeline estimates, milestones, KPIs or metrics that do make sense. The types of timeline estimates, milestones, and KPIs or metrics associated with AI projects are a bit different than the same things associated with non-AI projects.
· How does an AI system learn to become bias like this from data? The way an AI system stores words is using a set of numbers. The specific process for how these numbers are computed is quite complex and I won’t go into that here. But these numbers represent the typical usage of these words. So if, biased input information is stored then biased output would be the result. A second solution is to try to use less bias and or more inclusive data. AI team should have diversity to reduce bias.
· AI systems are susceptible to adversarial attacks, if someone else sets out deliberately to fool your AI system. One must consider that while building the algorithm. Read more on internet about deepfake, anti-fraud, anti-spam, fake comments, likes etc.
· I would advice most countries to use AI to strengthen what that country is good at and what that country wants to do in the future. There’s still plenty of room for every nation to learn more about AI, maybe even build up its own AI workforce and participate in a significant way in this AI powered world.
· AI and jobs- jobs that comprise more routine repetitive work are more amenable to automation, whereas many of the tasks that are less repetitive, less routine or that involve more social interaction with people maybe less susceptible to automation.
Lastly, I want to say to you, thank you very much for reading this.