How to start a career in AI (Artificial Intelligence)?

How to start a career in ai or artificial intelligence

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How To Start A Career In AI (Artificial Intelligence)?

Today, ‘Artificial Intelligence’ or ‘AI’ is probably the buzzword of all buzzwords. The term is everywhere. Just about every major tech company, whether they are American, Chinese, or European have embraced AI and machine learning as if it was one of the most important discoveries ever invented. As Blaise Zerega reports for VentureBeat, Google’s CEO even compared AI to the discovery of fire and electricity, and he has made Google an ‘AI-first’ company.

Amazon’s entire business is shaped by AI. Facebook uses ML algorithms to test out which of its AI and ML  algorithms are most useful and should be rolled out company wide. Adobe, a big player in the multi-channel marketing space, runs much of its Experience Cloud marketing platform through its Sensei AI product. Pegasystems, another Gartner leader in the marketing and robotic process automation space, also uses AI extensively in his marketing products. Even the analytics powerhouse SAS recently announced it will spend US $1B over the next three years on AI software and initiatives.
AI is, unquestionably, everywhere right now, but is this a case of chasing the horse after it’s bolted from the barn, or do abundant AI opportunities still exist? With most technologies, the first-mover advantage is substantial, but perhaps not with AI. The opportunity is so vast and so varied and crosses into so many industries that we’re only scratching the surface when it comes to AI right now. We might be at the end of the beginning, but we’re far, far from even the beginning of the end.

Why AI Is Revolutionizing the Tech Landscape ?

In its article Artificial intelligence Unlocks the True Power of Analytics, Adobe explains the vast difference between doing things in a rules-based analytics way as compared to an AI-powered way. For IT operations, conducting a root cause analysis can be a tricky endeavor, something similar to finding a problematic virtual needle in a vast tech stack. An analyst must manually investigate why an event happened and then consider all possible actions and remedies, which isn’t an easy task in today’s massive IT estates. An AI-powered analytics powered tool, however, automatically evaluates what factors contributed to an event and then suggests a cause and provides an action plan.

Keeping system Error Free

Some AI tools like AIOps solutions not only conduct a root cause analysis of a system but proactively work to keep the system error-free. Some are described as ‘self-healing tools’ since they constantly track the health of a system, automatically logging important information about the overall condition of the servers, databases, and software tools running on them.

Improve Customer Experience

AI can also be extremely useful for a company’s customer experience initiative as well, says Adobe. It can help entice customers to a company’s website, where website morphing can increase the chance of making a sale. AI can help with customer recommendations, as well as evaluate how effective a marketing campaign is. It can also find a company’s best customers as well as spot upcoming customer churn. In the current rules-based analytics way, a business analyst manually sets rules and weights to attribute the value of each marketing touch that leads to a conversion and/or a sale, claims Adobe. However, in an AI-powered analytics way, AI automatically weights and reports the factors that lead to successful marketing outcomes while also attributing credit to each step in the campaign process, explains Adobe. This makes the entire attribution process much more accurate and effective than the old way.

Customer Behaviour

AI is also great at both finding a company’s best customers and spotting ones who are about to defect to a competitor. In the old rules-based way, an analyst manually analyzes segments to understand what makes high-quality customers different, says Adobe. This compares to AI-powered analytics, which automatically identifies statistically significant attributes that high-performing customers have in common with each other. These can be used to find customers with similar attributes who should, if marketed to properly, become high-performing customers as well, or at least that the theory Adobe is putting its weight behind.
For reducing customer churn, Adobe claims the old way – analysts manually studying reports on groups of customers that have defected and trying to find patterns – will soon be eclipsed by the AI-powered way. In this methodology, the AI tool automatically identifies which segments are at the greatest risk of defection and why that might be so.
AI is radically changing the business landscape and tools like AI, robotic process automation (RPA), hyperautomation, and AIOps will automate away many of the jobs humans are currently doing, but, in many cases, these are not jobs humans enjoy doing. AI can remove the boring and repetitive work so humans can spend time on creative and higher-level work that has proven to be more profitable. For marketers, AI tools can automate away repetitive processes, like cataloging images or video and let humans do what humans do best — create.
There are so many use cases for AI, ML, and deep learning that it’s impossible to create an exhaustive list here, but it is particularly useful for marketing personalization, customer recommendation, spam filtering, network security, optical character recognition (OCR), voice recognition, computer vision, fraud detection, predictive asset maintenance, optimization, language translations, sentiment analysis, and online search, amongst many others uses. The opportunities are almost endless, but, as with all opportunities, there is a right and a wrong way to do things.

The next steps for Career In AI or Artificial Intelligence.

For those looking into a career in AI, the Internet is your best friend. AI, machine learning, and deep learning courses are available online from several universities, learning academies, and even some of the leading AI tech companies, including MIT, Stanford, Columbia, Udemy, Khan Academy, Google Academy, Nvidia Deep Learning Institute, Coursera, and Deeplearning.ai
Once you’ve honed your AI skills, you can toss your hat in the ring against professional analytics at communities like Kaggle, which have monthly competitions that help users can grow their data science skills. Teams can also compete, and recent prize money reached up to $100,000. YouTube is also filled with free tutorials on AI. Users can also create AI and ML models and then monetize them through Singularitynet.io, a website that dubs itself ‘the Global AI Marketplace’, where users can promote and sell their AI models.

Understanding various tools

Many of the tools needed for AI, ML, and deep learning are open-source, so free. Python and R are the most popular open-source solutions used for ML and they both have a large user base community. Scikit-learn combined with Pandas, Numpy, Seaborn, and Matplotlib make implementing ML algorithms in Python very versatile and these provide more customization and utilization than R. The R community has a large and motivated user base that shares its modeling libraries with the community.
Deep learning frameworks include Tensorflow, Caffe2, Keras, and Pytorch. All are open-source and have large, motivated communities built around them.

Data Science

AI and machine learning is nothing without data and if there’s one thing this world has created over the past decade, it’s data. Clive Humby calls data “the new oil”, but it’s far more abundant than oil. It is everywhere. And a lot of it is free. Google has a dataset search engine. Kaggle also provides datasets for analysts for free. The U.S. federal government provides scores of free datasets on a variety of federal statistics. The University of California Irvine has a free machine learning dataset. The Global Health Observation data repository is filled with worldwide health-related statistics for the World Health Organization. The FBI Crime Data Explorer contains a collection of crime statistics from universities and local law enforcement throughout the United States. Lionbridge.ai also has datasets for marketers, containing everything from social media ad campaigns information, to Yelp ratings, to a Google Analytics sample dataset, to Twitter customer support data.

Forums & Groups

There are plenty of AI and machine learning groups that interested parties can join to learn more about the tools as well as meet people who are working in the field. Facebook has several AI groups with robust and growing communities, including the AI and Deep Learning Group, the Data Mining/Machine Learning and Deep Learning Group, the AI Group. Rookies, however, might be interested in the Beginning Data Science, Analytics, Machine Learning, Data Mining, R, Python Group. LinkedIn also has several groups focused on AI, including the aptly named Artificial Intelligence Group, while others are more specific, AI Governance, Risks and Opportunities, Researchers, Faculty + Professionals, and Future Technology: Artificial Intelligence, Robotics, IoT, Blockchain, Bitcoin Group, and the Artificial Intelligence and Business Analytics (AIBA) Group.

Every company has big data in its future, and every company will eventually be in the data business.

Tom Davenport
 Today, if you’ve got big data you can almost guarantee that AI will be in your future as well, so the demand for AI will be there. 

So, what’s the best way to prepare for a career in AI.

Have no fear is a good start. Although it appears as though one needs a degree in calculus and/or applied mathematics to keep up with what’s going on, that’s not strictly true. Rauschenfels argues AI is a composite of three disciplines – computer coding, mathematics, and learning. It’s not for the uninitiated. You don’t need a Ph.D. to have a great career in this field. Most tools you need to get started are open-source and free, education is also, in many cases free. Datasets are abundant and highly available, even some highly specific ones.
As I previously mentioned, AI has probably eclipsed “Blockchain”, “Crypto”, and/or “ICO” as the buzzword of the day and this is the rare time when hyperbole has met reality. AI is being oversold but still, its long-term future can’t be undersold. For those worried that a lack of higher education could stifle their career rise in AI, Neil Raden, founder, and principal analyst, Hired Brains Research has some good advice when he claims, “Autodidacts—the self-taught, un-credentialed, data-passionate people—will come to play a significant role in many organizations’ data science initiatives.”

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