June 2021 -- Artificial Intelligence, Q&A with Ashish Nagar, CEO of Level AI
Insights, Noteworthy Bits, and Q&A with Industry Leaders
[Editor’s Note: Welcome back! This month we tackle the thorny topic of Artificial Intelligence and what it means for SMBs. Our friend, Ashish Nagar, the CEO of Level AI, provides practical insights for every business owner.]
Hardly a day goes by without a news article either exclaiming the virtues of Artificial Intelligence or warning of the dire consequences AI presents to society. Is AI “The Promise” or the beginning of the end? As with many technology topics the facts can become easily distorted and the truth depends on the lens through which you are looking at those “facts”. We’ll spare you the philosophical debate and focus on the practicalities.
So what do you need to know as a small business owner? What is AI? Should you care about it? AI is more accessible than you may realize as the technology has become embedded within products and services you already use, but it’s a near-impossible endeavor for a company to build AI capabilities directly. Thankfully, you don’t need a Ph.D. to leverage AI today. We’ll leave the nuances and complexity of artificial intelligence and machine learning to the academics and focus on exactly what you need to know.
What is Artificial Intelligence?
Artificial Intelligence is an extremely nebulous term, mainly because it’s a catchall phrase for software that attempts to perform human-level cognitive functions. For instance, telling the difference between a dog and a cat. Simple for humans, and until recently, extremely difficult for a computer. Generally when people talk about AI, particularly in business, they are referring to machine learning (ML) -- a process for gaining insights from crunching A LOT of data which can take the form of speech, actions, images, numbers, etc.
We are interacting with machine learning more and more in our daily lives, in ways we don’t even realize -- talking to Amazon Alexa, Netflix recommendations, even the auto-complete in Google Search! All of which means that yes, machine learning is accessible to everyone.
There are 3 levels of AI proficiency you can aspire to: Super Hero, Wizard and Yoda.
Super Hero
It’s pretty easy to be an AI Super Hero, in fact you probably already are one. Products you use today like Quickbooks use machine learning to categorize expenses and help reduce data entry. Or consider enhancing your web presence with a chatbot. Intercom has created an off-the-shelf product that will automatically resolve customer questions without human intervention -- with a simple connection to your website. While it can be counterintuitive, some of the best customer service experiences can be created without ever talking to a person.
Wizard
Like every great practitioner of magic, you want to level up your game. AI Wizardry comes from the deployment of AI solutions to optimize core business functions or even create a new product category. For instance, the 150-year-old Tennant Company, partnered with Brain Corporation leveraging their AI software and sensor system to produce an industrial floor cleaner that operates autonomously thereby creating an entirely new value prop for their customers.
It’s easy to think that machine learning is magical pixie dust you can sprinkle on your company but the reality is vastly different. Being a Wizard requires a concerted effort on the part of you and your team so don’t expect results overnight. Careful planning, implementation, and iteration will be required to fine-tune your new super powers.
Yoda
This is simple -- feel the force, but don’t try to be the force!
Yoda ML is for large enterprises that have significant R&D budgets to dedicate towards aggregating and cleaning huge data sets, developing proprietary algorithms and managing model deployment and operations. Even large enterprises with endless resources can fail to achieve Yoda status despite having a full lineup of machine learning engineers and data scientists on staff. Your focus should be on being a great customer of AI, but not a builder of AI.
So no, you’re not an AI company, nor should you be, but standing still is not an option either.
Conclusion
Here’s the deal -- Artificial Intelligence is everywhere and it’s not going away. You don’t need to become an AI company, but it is important (and possible!) for you to think like one. Here’s how:
Data collection and analysis -- you may not be generating terabytes of data to push through machine learning models, but creating a culture around collecting, measuring, and analyzing data can provide critical insights into your business. For instance, your website can provide you valuable data and insight to help you better understand your customers and your operations. You don’t need machine learning to be great at building a data-driven culture.
Be a Super Hero -- Take advantage of the billions of dollars being spent on the development of products and services that have machine learning capabilities baked into them. Life’s a garden, dig it!
Systems thinking -- Admittedly, this may be easier said than done. The digital nature of software gives it some unique advantages -- it’s configurable, dynamic, and once written, it can be copied and distributed at essentially zero incremental cost. Rather than being focused on physical inputs and outputs, rethinking your business as a system leveraging software and data to create new combinations of products and functionalities.
Once you have established this baseline of capability, a world of opportunity will open up especially as Silicon Valley continues to pour resources into making AI tools cheaper and more effective ultimately driving a compelling ROI.
Noteworthy Bits
Bonus Reading!
Three Major Fields of Artificial Intelligence and Their Industrial Applications
Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
Authors: Marco Iansiti and Karim Lakhani. A fantastic book for business leaders to consider how to integrate AI into their company strategy
State of AI Report 2020
Produced by AI investors Nathan Benaich and Ian Hogarth. A bit more advanced, but an excellent overview for those that want to dive in deeper
Andreessen Horowitz AI Primer
An awesome resource for non-technical folks who want to endeavor a few steps deeper into AI
Q&A with Ashish Nagar, CEO of Level AI
[Editor’s Note: We are delighted to have Ashish Nagar help break down Artificial Intelligence for us. Ashish is the Founder and CEO of Level AI, a company that leverages AI to help empower call center agents and managers with real-time knowledge support, monitoring and performance scoring. Prior to founding Level AI, Ashish was Product Manager in Amazon Alexa's Conversational AI team. He also helped build two Silicon Valley startups Kinestral Technologies and Relcy Inc. in a leadership role in the Business and Product teams.]
AI is often misunderstood — what is something you misunderstood or got wrong when learning about AI?
One of the key misunderstandings for me as a builder was that developing and improving AI products involved mostly inventing, developing, and refining proprietary algorithms. On the internet, you read about large models launched by OpenAI Google, and others, and you are transported to a world of advanced mathematics, abstractive work and design. The reality in my view is 20% of that, and the other 80% of the work in AI is really about the data which underpins any AI project. Building and maintaining clean data sets is at the heart of building and improving any AI system. There is a lot of engineering and product thinking which goes into building those and I often see that missing in AI projects, companies. As with any system, if you put crap in, you’ll get crap back out.
Is AI a risk or an opportunity for SMBs?
It is definitely an opportunity. SMBs have a unique opportunity to create efficiencies in their business, gather insights and delight their customers with AI. Currently, SMBs are better positioned than ever to leverage AI for their businesses. Over the last 5 years, the availability of off-the-shelf tools has increased. This enables SMBs with limited resources to get results with small technology teams or partners. For example, Google’s Auto ML tool allows model development, training and deployment of many AI models suited for a business in a very simple way. This was not even possible a few years ago. So access to these technologies is creating a very level playing field for SMBs.
Business owners have limited time and resources — how can they be leveraging AI / ML in their business today?
Data strategy
Companies of any size need to first have a data strategy before they have an AI product strategy. So very simply, look at all the input points of data potentially in your services and make sure that it is captured, stored in an accessible way for any future data analysis work.
Identifying opportunities for improvement
The next step after having a good system for collecting and maintaining data, in my view, is identifying which opportunities exist in your business where data-driven decision making could materially impact performance.
Hiring a small data science team
Often businesses are discouraged by the prospect of significant time and capital investment to spin up AI projects. With the current off-the-shelf tools available even a small 1-2 person team can create a large impact in an organization. Hiring data science talent also does not need to be about finding researchers in the space, as those tend to be extremely hard to hire and retain. Smart software engineers who are interested in analytics and decent training in mathematics are good candidates for initial data science team hires.
What new developments are on the horizon for AI / machine learning? What has you most excited?
Most of my product and business work in the last 5 years has been in the space of Natural Language Understanding (NLU). This is the branch of AI which deals with the problem of machines being able to understand human language. I am most excited with the prospect of advancements in this space over the last few years and what is ahead of us.
Understanding human language is one of the hardest problems in AI. Our language has humor, sarcasm, storytelling, hidden meaning built into it.
Over the last few years, the rapid increase in computing availability and large amounts of public language data have led to really large language models like BERT, GPT-3 possible. I am excited about the continuous advancement over the next few years.
I am looking forward to the time when a machine can summarize a short story by Salman Rushdie or Garcia Marquez in 300 words.
Help our readers cut through all the noise and B.S. What are the best resources you can point them to for learning more?
I have really enjoyed the Deeplearning.ai courses by Prof. Andrew Ng of Stanford. They are freely available on YouTube and Coursera. There is a course on ML for business people which is particularly good for business leaders to get started.
For beginning product managers, business executives who want to get into building and prototyping, I would look at the University of Michigan Data Science series on Coursera. It is a 2-3 month crash course to AI for dummies.
Finally, one of the books which I loved reading in this area a while ago was Life 3.0