Ben Taylor

About Me

Ben Taylor, co-founder & Chief Data Officer at ZIFF, Inc., is a thought leader with business use cases for deep learning, such as deep indexing, and large-scale training/deploy. He has filed eight patents ranging from machine learning to nanotechnology and appreciates the differences between IP and trade secrets. He is also a recognized expert on racist algorithms and best practices used to prevent them.

From 0 to $100K+ data science job in 6 months

I have loved being a data scientist. The job is challenging. The job market is great. I can't imagine any other job that would provide more career fulfilment for me now. This is my ideal career. Across the way, I see people that I care about trying to break into this field. Wondering what to study, what skills they need, feeling clueless about how to get that first real data science job in this market. This article is for them.

Why Most AI Projects Fail

Everyone has AI on their roadmap these days. Bottom-tier innovation verticals like HR, multi-level marketing, entertainment, fashion, medical, supply chain are even starting to talk about it. Despite the hype and excitement, the majority of companies that commit to tackling AI projects will fail. Even that $1M+ hire won't save you from failure. Here are some of the main reasons why your AI project failed or will fail. These have seemed to resonate well with others so I figured I would share.

Hard work won't make you a data scientist

Hard work has always been an important competency for aspiring students to become data scientists. Despite having studied there was still a noticeable gap between what they had studied and what industry wanted. You can be a great data scientist, but you can't if you stay in a silo. So going to Meet-ups, reading Kaggle forums, reading recommended data science books, following technical thought leaders, can help ensure you are at least heading in the right direction. Finding an industry mentor can also be very helpful. Lastly, fall in love with it. Passion for the topic and intrinsic motivation will help you stand out from the school of fish in the market.

Three Reasons You'll Never Be A Data Scientist

The most common questions asked is how do I become a data scientist?  It is a fair question for those that are deciding to pivot that direction because they want to eliminate the learning waste that traditional educations are full of. The number one reason I think you will never be a data scientist is your lack of passion.  Honestly, passion fixes everything. The biggest flaw in most people attempting to break into data science is their lack of breadth. People who are passionate have great breadth.

This Is Why Your Data Scientist Sucks

If you have a single data scientist and you already think they should be delivering more to your bottom line than they are news flash: "They suck" and you hired the wrong caliber individual for the job. You may still be able to keep them if they are good, but you need to bring in a type-E rockstar to cement your data arm and redirect the unstoppable ship. A type-E individual doesn't settle anywhere. If you ask an individual where do you see yourself in 5 years and they respond "Not working here" you have found a real winner.

The AI War Machine: AI Breakout

We can use AI to do things we don't want to. We can automate human processes, and allow us to focus on difficult problems instead of mundane tasks. How can you design AI that we can control? If you want to find a bug in your code when it comes to goal optimization run it through a genetic algorithm. The AI we design will always be focused on maximizing a goal. 

The AI War Machine: Our Darkest Day

Today we have AI metrics we applaud, like deaths-per-100-million-miles-driven. The metric sounds dark, but it is going down thanks to self-driving cars. The darkest metric will be discussed in private, away from the public eye. As AI war vendors compete on billion dollar contracts they will use kill-death-ratio (KDR) as a selling point. The Manhattan project of AI could be the physical modeling of the human body in these war simulations. The medical research efforts might accidentally enable a darker use case for the military. A big breakthrough with robots and AI is the ability to train them in a virtual environment. 

Smart Data Science Team Catastrophe

A common fallacy exists for people building data science teams that: smart hires translate to successful data science teams. What are the number one reasons you think smart data science teams fail to offer business value? The number one reason smart data science teams fail to win and provide value at the rate that they should is money. Sure you pay them well, but they just don't get the business drivers. They can't speak the language your board members, managers, and customers need to hear. Despite their data genius, they are idiots in the business world.

Getting That Data Science Job

Most people who attempt to get hired as a data scientist fail. This article is to help clarify what is happening and increase the chances of landing your first data science job. Everyone looks the same, literally. How do you expect a hiring manager to put you in their top 5-10 list for the final round if you look just like everyone else in the applicant pool? Once you admit that your fancy resume is actually boring and you look just like everyone else, you can start making some meaningful changes. 

"Your Expertise Is No Longer Needed" - Sincerely, DEEP Learning.

What does the human expert lack? Why can the world's best experts be beaten using subject naive methods? A human can't weigh historical observations fairly, they put too much weight on experience A and not enough on experience B. A human is also limited to their own personal experience and wisdom, where a computer can learn from more data than a human can see in a lifetime (medical imaging being a great one). Lastly, a computer can overtake a human expert's ability to experiment, do feature discovery, and validate new ideas. 

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