Curious about the future of Artificial Intelligence? Here are 51 predictions compiled by Gil Press.
My pick six:
Developers will confront the question of open sourcing their AI/ML data sets. It is no secret that companies like Facebook, Google and Amazon currently have a monopoly on our data. In 2018, developers will need to make a decision: band together and open-source their AI/ML data sets in the hope of standing up to these monopolies, or give in and resign to a future where Mark Zuckerberg and Sundar Pichai remain the keeper of the keys to AI innovation. One technology that will make these developer-led, open-source initiatives possible is homomorphic encryption. Through homomorphic encryption, AI/ML models can be developed and verified on a blockchain before being shared, in turn liberating them from today's limited and highly-centralized data sets. This approach paves the way to a more democratic and collaborative AI future while at the same time skirting any concerns with privacy and proprietary data — Matt Creager, Vice President of Growth and Developer Relations, Manifold
AI will become more accessible to non-experts… In 2018, we'll begin seeing two trends: AI interfaces will become so accessible that non-technical users across organizations and roles will be able to operate them. Additionally, more and more developers will begin learning how to program AI systems, making AI less obscure and rarefied and more a part of the standard developer toolbox — Tomer Naveh, CTO, Albert
In the same way that decades ago made it possible for any business to provide key information about the business 24 hours a day, we'll see bots start making it possible for businesses to provide answers to the most common questions their customers have. Natural language processing and machine learning will be increasingly accessible to even small and medium organizations — Dharmesh Shah, co-founder and CTO, HubSpot
Data is the foundation of digital transformation initiatives and we are sure to see more major brands across both business and consumer industries leveraging AI-driven metadata in the coming year. This data about data, unified across the enterprise, will enable organizations to start realizing AI’s full potential. By applying machine learning and AI to metadata across the enterprise, businesses will be able to more quickly and accurately capture unprecedented insights and make intelligent predictions based on data – including things they never thought to consider. This will fuel innovation, create better customer experiences, enhance security of sensitive information, and improve overall business outcomes — Anil Chakravarthy, CEO, Informatica
2018 is the year that AI becomes packaged and provided to the rest of us in ways that do not require a computer science degree. The goal isn't to create The Singularity – it's to make sound judgement scalable. Observe patterns. Learn from patterns. Apply and test guesses. Draw inferences. It's brute force-based, but it happens so fast you and I don't care – and it happens across a larger set of data than you and I could otherwise touch. But none of that matters if it isn't provided to other software – and users – in a format that can be used and can yield results. We're finally seeing APIs and client apps emerge that show we've hit that milestone — Mike Fitzmaurice, VP of Workflow Technology, Nintex
Data analysts begin to reap the benefits of AI… We’re getting closer to a place where data analysts leverage AI for pattern matching and conducting closed environment analysis. Soon, the job of analysts will be to point the AI to the right questions to be analyzed and to decide how to interpret the results in the real world — David Crawford, director of software engineering at Alation
What predictions do you have?
Learn more about artificial intelligence in the year 2017 by reading these stories from New Learning Times.