Artificial Intelligence

What is Hybrid Intelligence and Why is it the Future of AI


What is Hybrid Intelligence?

According to Moravec’s paradox, it’s easy to make computers perform intelligence tests or play chess, but difficult or close to impossible to impart the ability to perceive and mobilize like a one-year-old human child.

In the digital age, hybrid intelligence will do the significant heavy lifting, which brings together human and artificial intelligence to augment each other. Machine learning algorithms still struggle to apply knowledge to decision making, planning and creative activities and find it hard to adapt to dynamic environments.

Artificial intelligence is narrow. It may be good at conducting specific, defined tasks that can be mathematically computed and reasoned but fails on many fronts, so AI implementation has been slow.

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Why is Hybrid Intelligence Important?

Microsoft’s research titled Hybrid Intelligence and the Future of Work remarks that the performance of automation on perception tasks such as object and speech recognition is approaching human-level abilities. 

However, the research also notes that “AI systems still have limitations in carrying out complex activities that come natural to humans; machines are far from carrying out a natural dialog with humans or accomplishing a task like trip planning. They also make mistakes that can be upsetting or harmful for users.”

If you wonder how AI is deployed in critical industries such as medical diagnosis and autonomous vehicles, the answer is hybrid intelligence.

In April 2022, McKinsey integrated QuantumBlack, a “sophisticated analytics startup of more than 30 data scientists, data engineers and designers based on London”, and called it the “unified AI arm of McKinsey”.

According to Alex Sukharevsky, one of the leaders of QuantumBlack at McKinsey, one thing that didn’t change with the integration was their “original principle of combining the brilliance of the human mind and domain expertise with innovative technology to solve the most difficult problems. We call it hybrid intelligence, and it starts from day one on every project.”

Finally, for industrial analytics, hybrid intelligence is where it’s at. According to Francois Laborie for Forbes Technology Council, “if a predictive algorithm fails in the consumer industry, it’s not the end of the world. In asset-heavy industries such as oil and gas, power and utilities and manufacturing, failure isn’t an option.”

In these high-stakes industries, failure from AI can lead to equipment failure, a halt in a process or even life risk. Hybrid AI is the answer here for particularly these reasons:

  • Industrial equipment undergo operational changes over the years, so even though sensors may have been collecting data in industries, much less of it is for actual use. So, there are fewer resources for AI applications to learn from.
  • The quality of data from sensors and other IoT devices installed within industrial settings is low as the data is subject to harsh conditions and noise that biases data.

How to Get Started with Hybrid AI?

Hybrid AI makes artificial intelligence accessible to more and more high-stakes industries. However, here are the steps that further minimize any erroneous implementation of hybrid AI.

Lead with strategy

What are you trying to achieve? Hybrid AI will be a tool to tactically achieve objectives. How does your hybrid AI strategy align with your business objectives? Do you want to reduce operational costs or accelerate growth?

Think of the key questions before diving into implementation. Fit the technology to solve the problem rather than the other way around.

Get access to the data

Start considering the information you need to make AI answer questions, albeit with human intelligence integrated into the process. Data can be structured or unstructured depending on the use cases.

Sophisticated AI projects also work with streaming data in real-time, yielding current insights that are immediately actionable.

Implement the tools and infrastructure

What does your ideal infrastructure look like in hybrid AI? These tools may allow you to extract value from the underlying data quickly, saving you time and cost. Some of these systems may be simple for your internal teams to handle, and others may need external intervention.

Know the range of services and systems you need and how you would want it set up- on the cloud, on-premises or as a hybrid infrastructure.

Find or build relevant skills and talent

We find ourselves in the middle of an AI skills crisis. The industry implementing AI is moving faster than universities training people to work with them. In building hybrid AI, you will need domain experts as well as people with AI skills and knowledge.

You can choose to expand or upskill your workforce. Another great option is to hire outsourced consultants or teams to deliver their expertise at a lower cost than is required for hiring talent.

What Will Your Next Steps Be?

Combining artificial intelligence with human intelligence can open up newer, more reliable avenues for the application of AI systems. This also means that the demand for human intelligence and talent is likely to increase as hybrid intelligence gains prominence, which addresses concerns about unemployment stemming from automation.

Startups that can move early and fast to make AI more accessible by augmenting it with human intelligence will see success. For a detailed overview of implementing AI for your startup, download our latest white paper here.

There is a significant promise in hybrid intelligence and many important challenges. Get in touch with our AI & ML consultants today!

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