Michelin-star Executive Chef on creating world-class experiences around food.
Born in Queens, Akshay Bhardwaj studied business at Fordham University and Baruch College, and then pivoted to his passion: cooking. His ascension in the culinary world was extraordinary; between 2012 and 2017, he worked his way from working the line to executive chef at Junoon. Junoon was awarded one Michelin Star eight consecutive years from 2011, and held the title of the only Indian restaurant in New York City with a Michelin Star from 2018-2019. He was also selected as a Gohan Society Culinary Scholar — and traveled to Japan to study the delicate art of omakase — and became the first Indian chef to be selected for the Forbes “30 under 30: Food & Drink” list. Bhardwaj showcases a menu that reflects the diversity of India, steeped in the classics while offering deft touches of modernity.
Out to destroy pollution with advanced pulsed radio wave technology, serving people and seeing beyond first principles.
Dr. Srikanth Sola is the founder and CEO of Devic Earth, a Bangalore-based green tech company out to destroy pollution on this planet. You can find more about his venture at devic-earth.com.
Srikanth was a cardiologist at the Cleveland Clinic before moving to India and joining the Sri Sathya Sai Institute of Higher Medical Sciences, Bangalore. As a practicing cardiologist, he was stunned by the high morbidity and mortality due to air pollution, he began evaluating and developing technologies to improve air quality. After many successes and failures (which we will get into in our conversation today ;), he developed a pulsed radio wave technology that was inspired by the cardiac ultrasound he performed on a daily basis. This technology was highly successful and it compelled him to leave the full time practice of medicine to make it available to society on a wider basis. Srikanth has been named “Who’s Who in America”, “Who’s Who in Science and Engineering”, and “One of America’s Best Cardiologists” by the Consumers Research Council. He has authored 50 research publications in peer reviewed journals and numerous book chapters, and serves on the editorial board of several international research journals in cardiology.
When you cannot teach someone about something, that means you do not understand it. One way to understand something better is to start teaching it to people, learn from it and improve on it.
Someone (Einstein, Richard Feynman or somebody) put it well,
Contrary to that idea, to get better at anything we need to repeat it and rinse. So, instead of waiting to fully understand a subject before teaching it, one can start teaching it and the process of teaching someone clarifies things in our own head and improve our understanding of the subject.
I have come to this point many times. Sometimes, many times in a single day. I have a good idea and then think and think and think about it, imagine all the things that could go wrong and all the things that could go right. Weigh the pros and the cons in a rational way and of course the idea is to come to a reasonable decision. The problem here is that the original idea doesn’t actually get done, lot of thinking usually makes the idea not that interesting anymore and I lose the excitement I had in the beginning.
This line “If you want to do something, don’t think about it, go and do it” from Dyson vacuum founder in a podcast with Guy Raz made me think about this a bit. He has a point, why should you spend time thinking about the idea, what’s the fun in that, all the fun is in doing the idea and not thinking more about it.
On Creating Fuels and Chemicals For The Next 100 Years and The Importance Of Accessible Role Models
Karthish Manthiram is an Assistant Professor in Chemical Engineering at MIT. The Manthiram Lab at MIT is focused on the molecular engineering of electrocatalysts for the synthesis of organic molecules, including pharmaceuticals, fuels, and commodity chemicals, using renewable feedstocks. Karthish’s research and teaching have been recognized with several awards, including Forbes 30 Under 30 in Science.
On a mission to eliminate pregnancy related disorders in India and around the world, starting with early diagnosis of Preeclampsia.
Sumona Karjee Mishra is a scientist turned entrepreneur. She co-founded Prantae Solutions along with her husband to disrupt treatment of pregnancy related disorders, with an initial focus on Preeclampsia which affects 5-8% of all pregnancies worldwide. She received her PhD from the International Center for Genetic Engineering and Biotechnology, New Delhi.
“0 to 1 product management is simple but very hard to get right, billions of dollars and months of time and effort could be saved if PMs follow a few basic principles”
Does product management vary between before and after product-market-fit (PMF)? This is the fundamental question that led me to research this idea of 0 to 1 product manager and how his/her role differs from a PM that is trying to grow an established product.
What is the key objective for a pre-PMF product? It’s to find and solve a customer problem in a large market.
What is the key objective for a post-PMF product? It’s to grow the business. For example, you are an e-commerce company selling shoes online, you found your customers, you are able to serve them well (sales, marketing, support). Now, how can you grow this business? There are a few options –
Sell other products augmenting the shoe product line e.g clothing, caps
Sell shoes to more people (expand to other markets, e.g. international)
Sell analytics to healthcare companies e.g. training data
Product management can focus on creating new products to increase ARPU or introduce existing products to new markets (other countries, segments etc). When you don’t know that “shoe” is your product or “lack of shoe” is the problem to solve, does the PM role change in that context? i.e. before product market fit.
On a typical day, a PM makes many decisions, tradeoffs and prioritizes product features while keeping ROI at the center of it all. ROI framework works very well for a post-PMF product. For a pre-PMF product, if we try to optimize ROI, we will end up building a local optima. Yet we cannot ignore ROI, I have made this mistake a few times, “build it and they will come” may work but the question is “will they just come or will they also buy?”. What do I mean by that? Would I be able to convert the users to paid users? Am I providing value? Do they perceive value in the product that is worth spending money on? My point is, for a product that has not found PMF, while ROI metric is important, it alone cannot be the metric that’s driving decisions to help us find product-market-fit.
For example, at Akamai we developed a product that can be integrated into streaming apps to enable downloads for offline consumption. This is a great product, has a good market fit and a positive ROI. However, there are other factors that needed to be weighed in, competitive offerings, business model, long term company strategy, margin, customer delight. If ROI is the only metric we cared about, we would have pushed the product ahead but we decided to not pursue that product even though we had 10 paying customers and close to one million dollars in annual recurring license revenue in the first year of launch.
So how exactly is product manager’s role different before PMF? PM’s main objectives before PMF are to –
Identify a customer problem in a large market, and go solve that problem. This takes building, iterating, learning, listening to the customer, there is no overnight eureka moment here. The most crucial superpower any successful pre-PMF product team can posses is experimenting, learning from data (qualitative and quantitative) and quickly iterating to reach PMF.
Ensure that the customer is willing to pay for your product.
Be clear on who the user and customer are for your product, sometimes they are the same people and sometimes not.
“When you are before PMF, focus obsessively on getting to product/market fit. Do whatever is required to get to product/market fit. Including changing out people, rewriting your product, moving into a different market, telling customers no when you don’t want to, telling customers yes when you don’t want to, raising that fourth round of highly dilutive venture capital — whatever is required. When you get right down to it, you can ignore almost everything else. I’m not suggesting that you do ignore everything else — just that judging from what I’ve seen in successful startups, you can.“
Marc Andreessen – https://a16z.com/2017/02/18/12-things-about-product-market-fit/
Let’s unpack that to get a sense of what frameworks, metrics and tools a PM could use to get to PMF.
First, identify a customer problem in a large market, how to do that? Through build-measure-iterate loops. Running experiments, be willing to fail and learn what the market is telling us. What value we think we are creating and does the customer see the value as well? This is validating your value-hypothesis, I’m paraphrasing Andy Rachleff.
Second, ensure that customer is going to pay for the product otherwise it’s a nice hobby but not a business. Again test-measure-learn what pricing works, what business model works the best. Subscriptions? Transactional? Bundling with other services? Giving the product away for free in order to grow another north star metric?
Lastly, be clear on user and customer. Are you selling to the end user? Are you giving the product for free to users but charging your partners? Is your user the customer e.g. Netflix subscriber or is your user just a user and your customer is a small business e.g. EventBrite. Understanding this and setting the right priorities to delight both the customer and the user in different ways is important, ignore any one of them at your own peril.
“Your time is limited, so don’t waste it living someone else’s life. Don’t be trapped by dogma – which is living with the results of other people’s thinking. Don’t let the noise of others’ opinions drown out your own inner voice. And most important, have the courage to follow your heart and intuition.” __Steve Jobs
What’s your thing? Not your dad’s thing, not your neighbors thing, not your kids thing, not your grandma’s thing, not your manager’s thing but your thing, what is it? My sense is you can’t put a finger on it, so are about 7.5 billion others on this planet, you are not alone. I will take a wild guess and say that if you are asking this question, you are definitely in the 99.99 percentile of the world’s population (one of the 0.75 million people) who are likely asking themselves this question.
It’s easy to say “just be yourself”, “do your thing” but not easy to do it, otherwise the whole world would’ve already done it. The problem is not really in doing but really in developing that conviction about your thing. For example, I love writing, art, design, coding and running, a few things I really enjoy and wouldn’t mind spending a big chunk of my time in a day doing one or all of them. The challenge is two-fold, one believing that any of these things is going to pay my bills and take care of the family and even if I believe, how do I figure out which of these things is my thing? May be it’s a new thing that combines two or more of my interests?
Is the thing already within me and not something external that is magically going to appear one fine day? I think it’s latent and my job is to surface it, support it, like protecting a small fire and kindling it until it grows to become a huge fire. It’s a marathon. It’s a process, day in and day out.
Once I figure out what the thing is, then the next step is to develop conviction/faith in it. How does one develop conviction in something, isn’t it through experimentation? Can faith develop overnight? Is it a flip of a switch? I don’t think it is, faith develops over time, as we see small progress being made each day. For that kind of experimentation to happen, one must be ready to experiment, fail, learn and try something new the next day. How do you know if you are failing or succeeding? It can’t be done inside your own mind, your thing has to come in contact with the people you are trying to serve. They have to provide that feedback and that feedback becomes the life blood in refining your thing. All of this takes time, practice, discipline, daily hustle, daily communication with your users.
Someone put it like this, “How much suffering are you willing to take”, may be that’s it. How much sacrifice and suffering are you willing to go through to accomplish your thing? Embarrassment, cold-calling people, falling flat on your face, feeling uncertain, feeling fear and yet moving on, what a way to keep it real, that’s why I love entrepreneur’s journeys. It’s the ups and downs and facing harsh realities, facing one’s self, facing the dark abyss, the feeling of eating glass every day for breakfast, lunch and dinner.
I digress, so how do you surface “your thing” to the forefront of your day, how do you make it your thing everyday and live it, breathe it and do it? Are there rituals, practices and tools to help you work on your thing daily? As Stephen Covey put it, “The main thing is to keep the main thing the main thing.” and to make your thing the main thing is the main thing.
In contrast, AI research has been in the works for many decades starting in the 1950s, there have been as many if not more libraries created in the past 10 years as there were programming languages created in the last 50 years.
Here is a chart of AI libraries and how many people “follow” them on github, interestingly, newest of the libraries, TensorFlow seems to be a few orders of magnitude more popular than the others. That doesn’t mean it’s the best AI library, in fact there is no such thing as an AI library (general purpose). My sense is that there are libraries that assist in developing AI applications and some are better suited for an application than others, depending on the problem being solved e.g. computer vision, natural language processing
Let’s take a quick look at what each of these libraries are suited for
scikit-learn – machine learning library of algorithms for data analysis and regression in Python
BVLC/Caffe – Berkley Vision and Learning Centre’s Caffe is a deep learning library for processing images. Caffe can process over 60M images per day with a single NVIDIA K40 GPU
Keras – a deep learning Python library that runs on top of TensorFlow, Theano or CNTK. Primarily an experimentation framework assists in fast experimentation with models.
CNTK– Microsoft Cognitive Toolkit is a deep learning library that can be included in Python, C# or C++ code. It describes neural networks as a series of computational steps in a directed graph.
mxnet – A flexible and efficient library for deep learning.
“Deep learning denotes the modern incarnation of neural networks, and it’s the technology behind recent breakthroughs in self-driving cars, machine translation, speech recognition and more. While widespread interest in deep learning took off in 2012, deep learning has become an indispensable tool for countless industries.”
PyTorch – is an open-source machine learning library for Python, based on Torch, used for applications such as natural language processing.
PyTorch is a Python package that provides two high-level features:
Tensor computation (like NumPy) with strong GPU acceleration
Deep neural networks built on a tape-based autograd system
Theano – is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
Caffe2 – aims to provide an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms.
Torch7 – is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT
Ok great, this is a list of a few of the hundreds of AI libraries, so what? I can google them myself, what’s the point of this blog? I’m as deluded as I was before reading this blog, if I am just starting out in AI, which library should I pick, where should I start? The short answer is pick any library, you will be better off picking one and running with it and developing something than not picking any, you have to do it, might as well start now than later.
Of course a better answer might be, what do you seek to solve? Are you looking to programmatically recognize people’s faces or cars in a photograph of a busy street? You might want to start with the BVLC/Caffe. Here is a good presentation to get you started on Caffe