Home > Machine Learning and Artificial Intelligence Trends

Machine Learning and Artificial Intelligence Trends

2019 has been a critical year for Artificial Intelligence (AI) and Machine Learning (ML) technologies as real-world industry applications demonstrate their hidden benefits and value to the consumers. So far, scientists and researchers have made claims on behalf of AI-enabled technologies, but they have not been tested in large-scale market applications. We will see a lot of those technologies put into marketplace practice for the users to judge and evaluate.

The rapid adoption of AI across global businesses will help the AI market reach about $13 trillion in 2030. However, McKinsey adds a warning note that high adoption rates may lead to severe performance gaps between the successful AI implementers and non-implementers. Advanced AI technologies not only have the potential to widen the affluent countries from the under-developed ones but can also threaten jobs in affluent societies.

A solid Business Intelligence trend that started about two years ago will reach its peak in 2019, thanks to the advancement of AI and ML technologies. Business Analytics in 2019 will not only help democratize Data Science among business users but also enable complex, large-scale analytics with fast, accurate results at a lower cost.

Artificial Intelligence and Machine Learning Trends in 2019

2019 signals the beginning of the “digital revolution” slated to transform global businesses from the grassroots. So, let’s see how the above will start triggering the front-running AI and ML trends next year:

  • The exponential growth of business data, low-cost data storage, and AI reaching maturity will lead to more businesses outsourcing their data center enter activities to cloud service providers.
  • The easy availability of both live and dead business data will contribute toward the creation of better Machine Learning models and algorithms. The algorithm marketplace in 2019 may create more opportunities for AI and ML researchers to interact with business practitioners to build real-world solutions.
  • Wearable devices and the development of intelligent apps will rise in tandem, with the increasing rise of mobile consumers.
  • Major companies like Amazon, IBM, Microsoft, and Google already offer virtual agents to consumers, and this trend may be picked by other businesses next year.
  • The use of Natural Language Processing (NLP) will rise significantly in customer-service functions that require text processing at scale.
  • Blockchain will move beyond banking-finance domains and into many other industry sectors.

A Summary of the Trends

Trend 1: With data flywheels dominating the 2019 business ecosystem, data acquisition and storage costs will drop significantly. This trend, in turn, will lead to easy access to ML algorithms hosted on the cloud. The pre-trained ML models will enable every business to tap into a readymade platform of transferable intelligence and analytics at scale.

Trend 2: No amount of data can be useful to a business unless powerful algorithms can extract the necessary insights from them. In 2019, as the algorithm economy takes over the traditional management of business processes, every business will turn into a data company, where the scientific and research communities can directly interact with business leaders and operators to jointly find business solutions.

Trend 3: The newly rising app store is akin to the algorithm marketplace, where every person is an app innovator, buyer, or seller. Alexander Linden, research director at Gartner, thinks that in that environment, an app innovator will not need “sales, marketing, or distribution channels” to sell their ideas or products.

Trend 4: Many players are using virtual agents for low-cost customer service today. The virtual agent is usually programmed to provide basic customer-support services to customers. The main idea is to make the customer feel they are talking to a real person while saving operational costs.

Trend 5: This technology provides machines the power to convert data into text, which is widely used to convert customer feedback into written summaries or reports. With open-source, Machine Learning, and Deep Learning frameworks in the future, the smart models will be able to do more like tagging images or recommending products.

Trend 6: Blockchain applications have been tested in healthcare, insurance, cyber-security, contract management, and many other industry sectors. The results of these pilot applications may be available next year.

Trend 7: Industry literature seems to indicate those particular manufacturing units, supply chain, and logistics have already deployed and successfully utilized robot bosses. (How will you feel if tomorrow you go to work and find out that your new boss is a robot?)

Trend 8: Increased automation is a rising concern for business operators and employees. Will AI and associated technologies outperform the human workers, making them redundant? The fear is natural and real. The job-cut and downsizing trends are already there, and a gloomy 2025 forecast by industry watchers like McKinsey and Gartner has left people uncertain. Will 2025 be the death knell for human workers when robots will take over all business functions efficiently?

Trend 9: These statistics may be reassuring for those worried about losing their jobs to machines in near future: According to McKinsey, machines will possibly “augment employment by around 5 percent by 2030, as well as improve productivity by about 10 percent.” Will it be a human-machine collaboration?

Trend 10: Tesla has more than 780 million miles of driving data, which is fed to the main computer inside the self-driving car through radars, sensors, and cameras. Google has just over 1.5 million miles of driving data. In the self-driving world, the driving data is used to navigate the car, change lanes, or avoid a collision.