AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019

As in the past, we bring you a roundup of predictions and analysis from experts.

We have asked

What were the main developments in Data Science and Analytics in 2018 and what key trends do you expect in 2019?

Here are part of the answers we received. Read the full post on KDnuggets

AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019- 

https://www.kdnuggets.com/2018/12/predictions-data-science-analytics-2019.html

with answers from Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.

Although we asked about Data Science and Analytics, AI was the dominant topic in most answers. Key themes touched by these experts include AI advances, both real and hype, Democratization of Data Science and Analytics including self-service, Automation of everything including Data Science, GDPR, AI Risks, real-time analytics, and more.

Tom Davenport, @tdav, is the President's Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative on the Digital Economy, and a Senior Advisor to Deloitte Analytics.

We predict annual trends at the International Institute for Analytics, and here are a couple that stand out for me:

• Organizations are paying increasing attention to model deployment rates - According to the Rexer Data Science Survey, only 10-15% of companies "almost always" deploy results and another 50% only deploy "often." That leaves 35% - 40% of companies that only occasionally or rarely successfully deploy analytical models. I have encountered some organizations that say their successful deployment rates are less than 10%. Of course, there is no economic value to an analytical model that isn't deployed. Companies will need to measure and improve their deployment rates in 2019.
• Citizen data scientists and business analysts are here to stay. The rise of graphical and search-based analytics, as well as increasingly automated machine learning on the data science front, mean that we will see increasing amounts of analytical work done by amateurs. Fighting the trend is a losing battle, so focus instead on enabling it and putting guardrails around it. It also means that quantitative professionals will either need to move toward highly complex and difficult modeling tasks, or toward understanding business problems and addressing organizational change.

Carla Gentry, @data_nerd, is a consulting Data Scientist and Owner of Analytical-Solution.

2018 was a stellar year for analytics and data science but we also saw the explosion of AI, Neural Nets and Machine learning, with and without the talent and or experience to back up claims. We saw an increase in the use of AI in the medical field and policing, again, with and without dangers of bias, talent and or experience, I think some have forgotten data equals lives in these instances and with wearables and IoT (Google Home, Alexa, etc.), expect that to continue.

2019 will be more of the same buzzwords and companies will start to realize it takes neural net thousands or millions of examples to learn from, what's worse, each time you want a neural net to recognize a new type of item, you have to start from the beginning (time consuming to say the least) - Talent is another issue, besides Geoffrey Hinton, Yejin Choi or Yann LeCun you really aren't an expert in neural nets, so don't expect a big talent pool to hire from.

Data Science is about gleaning data insight and in some cases, it's not correct to expect us to be experts at AL, machine learning, or neural nets, so the differences will have to be more carefully explored and novice users will have to reskill to compete in this new future of tech. My fear is that a lack of true understanding of how machines learn and how artificial intelligence can be used without harm will continue to expose weaknesses with some companies/algorithms/firms.

Let's cheerfully move forward with all these technologies but understand, there are consequences if you get it wrong! 

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Cassie Kozyrkov, @quaesita, is Chief Decision Engineer, Google Cloud. Loves Stats, AI, data, puns, art, sci-fi, theatre, decision science.

One of the major developments for 2018 is the democratization of data science. From cloud technologies, which allow people to give resource-intensive big data and AI applications a whirl without having to build a data center first to tools like Kubeflow which bring scalable data science to folks without infrastructure expertise. This trend towards tools that make data science accessible to everyone will accelerate even more in 2019. 

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Gregory Piatetsky, @kdnuggets, is the President of KDnuggets, Data Scientist, co-founder of KDD conferences and SIGKDD, and no. 1 on LinkedIn 2018 Top Voices in Data Science and Analytics

Main Developments in 2018:

• GDPR, which took effect in May 2018, was a significant milestone not only in Europe, but also in the US and other areas, with many companies updating their privacy policies. However, it remains to be seen whether there will be actual improvement in consumer privacy or it will be business as usual under the cover of new privacy pages.
• Democratization of Data Science continued, with many more tools wider giving access to Data Science insights. I note major new tools announced at AWS reinvent.
• AI Risks: First fatality from a self-driving car happened when a self-driving car was confused by a pedestrian walking with a bicycle. This increased spotlight on inevitable risks of AI. At the same time, self-driving cars (and automated AI) should not be held to an impossible zero errors standard, but compared to current risks. For example, human driving is extremely dangerous, with 37,000 traffic deaths just in the US in 2017.

Key Trends for 2019

• Data Science Automation will continue at accelerating pace, but Data Scientist jobs are safe from full automation at least for the next few years.
• AI progress and Hype: while AI progress is real, AI Hype will grow even faster
• China has become a major player in AI, with many Chinese firms doing their own innovations and not just copying from the US.
• Reinforcement Learning will play an increasingly central role in AI progress. See for example amazing progress of RL in solving Montezuma's Revenge Atari game, reaching level 100 and exceeding by far all previous records, computer or human, in this game. 

Read the full post on KDnuggets: AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019 

https://www.kdnuggets.com/2018/12/predictions-data-science-analytics-2019.html

with answers from Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.