2018: What Mattered in IT?
Artifical Intelligence

2018 in Machine Learning News: A Year of Reckoning

In a lot of ways, machine learning news in 2018 suggests a year of reckoning with the technological advancements of preceding years. The push toward driverless vehicles slowed after a death; ethical concerns about AI were addressed by no less than Microsoft and Google; ML-driven tech became increasingly ubiquitous at home and at work, even as people grew increasingly concerned about data protection.

For reporters covering data privacy and machine learning news, 2018 was a year with a lot of big headlines.

The latter beat moved increasingly from specialized coverage to the mainstream as vendors brought AI and ML into both the home and the workplace.

Industries continued to embrace AI--with mixed success. At the same time, solid advancements were made in both facial recognition and natural language processing, with impacts both everyday (your digital assistant gets smarter all the time) and high reaching and far-ranging (with law enforcement agencies exploring new ways to leverage the facial recognition technology).

In data privacy, the year was marked by efforts to restrict and control data collection, thanks to increased consumer concerns and new legislative restrictions on how organizations can collect data and what they can do with it. This circles back to AI and ML because, of course, computers need a lot of data to fuel such technology. Indeed, this year the possibility of less data, or less access to it, sparked concerns about development in the AI and ML fields.

In a lot of ways, 2018 was a year of reckoning with the technological advancements of preceding years. The push toward driverless vehicles slowed after a death; ethical concerns about AI were addressed by no less than Microsoft and Google; ML-driven tech became increasingly ubiquitous at home and at work, even as people grew increasingly concerned about data protection.

For machine learning news in 2019, watch for new restrictions on data use and collection, the ongoing reckoning with the ethical implications of widespread AI, and the struggle to balance a desire for ML-driven technologies with the limited workforce available to implement them.

Here are some of the items of machine learning news that best encapsulate 2018 in AI, ML, data privacy and big data, with a look ahead to where those stories might go in the next 12 months.

GDPR 

The news: In the spring of 2018, inboxes were full of emails from various companies, reassuring users about how their data was being used. The catalyst for this was the adoption of GDPR--or Global Data Protection Regulation--across the EU, which went into full effect in May.

Why it mattered: Many companies operate worldwide now, and GDPR is binding against any entity that possess, retains and processes consumer data from the EU. Companies had to get in line with the new restrictions, some of the most stringent yet for consumer data, even if physical headquarters were located outside the EU.

How it affects IT: Consumer data is not just used by organizations to identify users or sell them things. AI needs big data to learn, and restricted access to such data is a challenge for the development of AI technologies. At best, GDPR meant a lot of logistical headaches for IT professionals working to get their organizations in line; at worst, it might hamper the AI implementation that many industries say is essential to their futures.

For 2019: GDPR is just the start, and the regulations are expected to be at the forefront of a new wave of increasing protections for consumer data. Watch for the ongoing implications of GDPR throughout 2019, as well as a push for similar--or even tougher--rules in other jurisdictions.

Natural Language Processing

The news: New advancements in natural language processing are furthering the reach of AI and ML into homes--and, increasingly, businesses. For example, Microsoft highlighted NLP in Cortana at Ignite in the fall.

Why it mattered: Amazon and Apple, through their Alexa and Siri digital assistants, have already worked to make NLP a home-based solution for a variety of organizational problems. As the technology behind NLP becomes more advanced, it can increasingly be relied on in workplaces.

How it affects IT: NLP can automate many basic business operations, and unlock new functionality in already popular applications. IT departments will need to be ready to integrate the tech, and keep it reliable and secure.

For 2019: Watch for machine learning news from Microsoft as it is likely the company, along with other tech firms, will continue to expand AI’s reach into the business environment through NLP integration. And some hope that NLP will solve problems as well as provide solutions--for example, detecting hate speech

China

The news: As Donald Trump’s administration increased both restrictions on immigration and trade tensions with China, concern about how the political climate would affect technological development in the United States. grew. At the same time, China is putting AI- and ML-driven technologies like facial recognition to new uses--some of them controversial.

Why it mattered: Chinese nationals gain valuable working experience in the United States, among other countries, and the potential loss of that opportunity could harm AI development in China. At the same time, if people leave opportunities in the United States to return to China, that country’s tech sector might benefit.

How it affects IT: Unhampered by ethical concerns that restrict the use of technologies like facial recognition in the United States, China could prove to be a predictor for where the technology could go-- or it could be a cautionary tale. At the same time, firms may face staffing issues if trade escalations continue.

For 2019: At press time, Trump was hinting that he would interfere in the Canadian arrest (at American request) of a top Huawei executive in exchange for political concessions. China, meanwhile, continued to push ahead on AI development, without the data collection restrictions other countries have. This could go in a variety of directions in 2019 but it’s an area of machine learning news to watch either way.

 

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