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, bringing human and artificial intelligence together 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 it also fails many times.
In this article, we will unveil the importance of hybrid intelligence, the hype around it being the future of AI, and its use cases within various industries.
We’re going to discuss:
Consider a situation where an artificial intelligence system (a BOT) does not understand the term “context.” At such points, human supervision or assistance is required to allow the bot to proceed and ensure that the end user receives a satisfactory response.
This is where hybrid intelligence enters the picture. Hybrid intelligence combines the strengths of human intelligence and artificial intelligence (AI) to produce results that ensure an accurate understanding of the context.
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.”
The answer is hybrid intelligence if you wonder how AI is deployed in critical industries such as medical diagnosis and autonomous vehicles.
In April 2022, McKinsey integrated QuantumBlack, a “sophisticated analytics startup of more than 30 data scientists, data engineers and designers based in 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.”
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 for these reasons:
Related reading: Should You Indulge in Conversational AI for Your Startup?
New disruptive technologies and data appear everywhere, radically altering all industry sectors. The resulting wave of transformation will reshape competitive landscapes and redefine industry boundaries for the foreseeable future.
Medicine research, advanced manufacturing, telecommunications, energy, and autonomous transportation are just a few industries that have already made significant investments to accelerate progress in this area.
On the other hand, Hybrid Intelligence is equally applicable and crucial to all industries where high-risk/high-reward expert decision-making plays a role.
1. Intelligence Industry
Data integration, deployment, tooling, technology selection, change management, and strategy are all part of the intelligent industry.
For example, a pharma R&D drug discovery transformation could entail implementing various technologies and techniques such as data analytics platforms, automation, collecting and purchasing new data sets, mRNA, and new data architecture. All this is to collaborate smoothly within and beyond the department and organization.
2. Crisis Management
Hybrid intelligence-powered crisis management systems would significantly transform Crisis Management Hybrid intelligence-powered crisis management systems. Multidimensional big crisis data informatics encompasses massive amounts of data and diverse data sources (which can consist of various data types).
Each of these large-scale crisis data sources provides a distinct (but necessarily incomplete) view of what occurred and why it occurred on the ground.
Hybrid AI makes artificial intelligence accessible to more and more high-stakes industries. However, the steps further minimize any erroneous implementation of hybrid AI.
Lead with strategy
What are you trying to achieve? Hybrid AI will be a tool to achieve objectives tactically. How does your hybrid AI strategy align with your business objectives? Do you want to reduce operational costs or accelerate growth?
Think of the critical 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 quickly extract value from the underlying data, saving time and cost. Some of these systems may be simple for your internal teams, and others may need external intervention.
Know the range of services and techniques 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 and 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.
Combining artificial intelligence with human intelligence can open up newer, more reliable avenues for applying AI systems. The demands for human intelligence and talent are 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.
Hybrid Intelligence requires human skill, creativity, and technical mastery to deliver these complex programs. We investigate unusual and complex data challenges that require a thorough understanding of the context and business requirements. Our team members have diverse software, data, applied, and theoretical sciences, engineering, and technology skills.
For example, we combine knowledge of how to build AI-enabled systems with domain, cultural, and business process knowledge. Finally, we use our expertise to create strategies that achieve business objectives while remaining sustainable, fair, accountable, transparent, and safe.
There is a significant promise in hybrid intelligence and many essential challenges. Get in touch with our AI & ML consultants today!