2017 Tech Trend Predictions: Year-End Grades

POSTED BY on 11.16.2017

At the start of the year the IEEE Computer Society predicted what would be the top tech trends for 2017. The trends were picked by a committee of member volunteers led by Dejan S. Milojicic, who was the 2014 IEEE Computer Society President. The trends focused on industrial IoT, artificial intelligence, machine learning, cognitive computing, 5G, accelerators, and others.

With the year coming to an end, the committee decided to look back and see how they did. “The past year has seen dramatic adoption of deep learning and rapid introduction of cryptocurrencies and blockchain technologies,” said Milojicic. “We have not seen such innovation dynamics in a long time, all setting the stage for an even more promising 2018.”

For each prediction, the committee provided a grade and an analysis of said grade.

Industrial IoT:  B+

Of all IoT efforts, industrial IoT gained the most credible advances over the past year. The reason for the B+ grade is that it has not yet reached our expectations for broad adoption.

Self-driving cars: B-

Self-driving cars continued to improve, but wide adoption is still hampered by legal, ethical, and, to the least extent, technological advances. This resulted in a lot of negative press. It did, however, help with assisted driving.

Artificial intelligence (AI), machine learning (ML), cognitive computing:  A+

Deep learning in many technology areas contributed most to adoption, hence the A+ grade. It is starting to be utilized in many other technologies, including at the edge of the network and in data centers, and is paving the way to broader adoption of AI and ML.

5G: B

5G continued advancing, but it stillinitial deployments. Broad adoption is still a question given the advances of the existing standards.

Accelerators: A

Accelerators have been the foundation of many of the advances of deep learning. They have been deployed in the form of ASICs, FPGAs, and GPUs in data centers (Google, Microsoft, and Amazon) as well as at the edge of the network.

Disaggregated memory/fabric-attached nonvolatile memory (NVM): C+

The opportunities of NVM continue to promise innovative disruption up and down the software stack. However, the properties observed in early devices have not shown a compelling economic value, and have caused delays in delivering the core technology by all the major vendors. This resulted in a low score of our prediction. We still expect that NVMs will be on the upswing once hardware products become available.

Sensors everywhere and edge computing: A-

Sensors continue to gain wide adoption at home, in industry, in transportation, in smart tools, etc. Edge computing enabled by accelerators is gaining momentum across industries. There is, however, still more talk than actual deployment this year.

Blockchain (beyond Bitcoin): A

It appears that blockchain is gaining momentum, both in industry (startups and mature) and academia (see IEEE Spectrum October issue.) We will see if this momentum continues next year and gains more adoption.

Hyper-converged systems: B

These had a solid year, and the reason for not giving our prediction an even higher score is that they still have not delivered the “software-defined everything” vision.

Overall Score:  A-

Looking back, we correctly predicted most while only missing a few.

Click here to view the press release announcing our predictions for 2017.

Look for an announcement early next year for what the committee selects as the top trends for 2018.

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