Machine Learning and Blockchain for PMs by Director of Product at Target
Aarthi Srinivasan has had major experiences across the tech world. Now a Director of Product at Target, she will explain what machine learning and blockchain mean for the future of digital business.
In the past, she has worked for leading companies like Oracle, IBM and JPMorgan Chase. She is currently invested in exploring the possibilites of new technologies, and the challenges they present to product managers and other business roles. Read and learn how to harness machine learning and blockchain!
Meet Aarthi Srinivasan
Aarthi Srinivasan is currently Director of Product Management at Target, in charge of Personalization, Machine Learning and Blockchain. In the past, she has held senior PM positions at companies like Oracle, IBM, JPMorgan Chase, Intuit, Financial Engines and WalmartLabs. She has been a speaker and written articles for Product School, the ProductCon Conference and many other tech communities. She’s therefore well-versed in all aspects such as product strategy, product definition, lean testing and data analysis.
Machine Learning and Blockchain for PMs
Where do you stand on Tech Singularity? That is, the oncoming revolution brought by exponentially powerful technologies such as artificial intelligence, automation and blockchain. From the scepticism of the late Stephen Hawking, to the full embrace of Mark Zuckerberg, there are all shades of attitudes towards this transformation. Aarthi will make clear how to harness it for good; particularly to grow more as a business, that services both our needs and the needs of our customers and users.
Aarthi Srinivasan‘s Insights on Machine Learning and Blockchain for PMs:
- “Voice will be the new text. If you see the younger generation, nobody is typing and writing their messages. They’re all talking with their device”
- “We will have artificial superintelligence, but we need to do it with ethics”.
Highlights on Machine Learning and Blockchain for PMs
Artificial Intelligence: Let’s Get on the Same Page
- Artificial Intelligence: The capability of a machine to imitate intelligent human behaviour.
- Machine Learning: Getting computers to learn or recognize something without being explicitly programmed.
- Deep Learning: Type of Machine Learning that can process a wider range of data resources, requires less data preprocessing by humans.
Types of Machine Learning
- Unsupervised Learning Algorithms: This includes descriptive ones, which look into the past; predictive, understanding the future and prescriptive, that advise on outcome.
- Supervised Learning: There are plenty in this category. Some examples include: linear regressions (price of a diamond by size, shape, etc.); logistic regression for classification (benign or malignant diagnoses); linear quadratic analysis (sale closing probability); decision trees (hiring processes)…
Types of Deep Learning
- Convolutional Neural Network: A multilayered neural network designed to extract increasingly complex features of the data at each layer to dermine the output.
- Recurrent Neural Network: A multilayered neural network that can store information in context nodes, allowing it to learn data sequences and output a number or another sequence.
Blockchain Technology Platforms and Future
- A Blockchain is a growing list of digital records or blocks that are secured and linked. Each block contains a hash value link to the previous block, a timestamp and data.
- They can be permissionless (anyone can participate and validate) or permissioned (where only authorized actors can validate a block.
- The different platforms have a combination of distinct traits: Bitcoin, Ethereum, Hyperledge and R3 Corda are some examples.
The Ethics of Machine Learning and Blockchain for PMs
- Future blockchain applications will include virtually anything from identity platforms, our social media apps and (sooner than we think) environments like Medical AI contracts, Internet of Things safety and security or finance protection contracts.
- The so-called “tech singularity” can only be achieved if a corresponding code of ethics for machines and their creators is developed simultaneously.