Roadmap to Artificial Intelligence Adoption — The Collaborative Intelligence: AI to enhance Humans
“Human-only jobs are dead.”
As stated in the previous article “Roadmap to Artificial Intelligence Adoption — Understand AI”, Artificial Intelligence (AI) is becoming skilled at many human jobs, and it is progressing fast. This improvement is fostering reasonable concerns that AI will ultimately replace human workers throughout the economy. But that is not the inevitable outcome.
It is the time when AI, or the technology in general, serves human and business needs. Never before have digital tools been so responsive to us, nor we to our devices. AI will radically remodel how work gets done and who does it; however, the technology’s more considerable impact will be in complementing and augmenting human capabilities, not replacing them.
On the other hand, companies that implemented AI (and other technologies) to automate processes and displace employees without a clear digital vision will face only short-term productivity gains.
That is why several investigations demonstrated that organizations achieve the most significant performance improvements when humans and machines work together.
In fact, humans and AI actively enhance each other’s complementary strengths: leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter.
What comes naturally to people (e.g., making a joke) can be tricky for most machines, and what is straightforward for machines (e.g., analyzing gigabytes of various data, aka Big Data) remains virtually impossible for humans.
To take full advantage of this collaboration, businesses require both kinds of capabilities. Companies must build a Collaborative Intelligence System by understanding how humans can most effectively augment machines, how machines can enhance what humans do best, and how to redesign business processes to support their relationship.
AI Machines need Human support
As for Machine Learning algorithms, Data scientists must train machines to perform certain tasks and make complex decisions.
Besides learning from data, AI systems must be trained on how best to interact with humans. Just think about the voice assistants from Google and Apple: the AI requires extensive training to develop just the right personality: confident, caring, helpful but not bossy, and all of this reflecting its companies’ brand.
Human help is crucial when the machine has to deal with more complex problems. Sometimes the results given by the AI are counterintuitive or controversial, so the process needs someone that interprets and explains how the AI weighed inputs to make a certain decision. Consider all the possible cases between insurers and law enforcement: it is fundamental to understand why an autonomous car took an action that led to an accident or failed to avoid one.
In fact, the new General Data Protection Regulation (GDPR) of the European Union gives consumers the right to receive an explanation for any algorithm-based decision.
And lastly, when we talk about real robots (like those by Boston Dynamics), people have to sustain the responsible use of machines (e.g., preventing robots from harming humans).
From on hand, AI-equipped robots that work close to humans should ensure they recognize humans nearby and do not endanger them. On the other hand, AI systems should embrace ethical norms and not be biased by the developers’ decisions, i.e. how they developed the algorithm.
Since the latter has happened in the last years, companies require the role of an ethical manager that is responsible for investigating and addressing the ethical problem.
The previous paragraphs are just an example of how many new job roles AI could generate. Companies must develop a solution through co-creation — having stakeholders envision how they might collaborate with AI systems to improve the process.
AI-Empowered Humans
The symbiotic relation indicates that intelligent machines are helping humans expand their abilities both by amplifying their cognitive strengths and by embodying human skills to increase their physical capabilities.
Artificial intelligence can support and boost our decision-making abilities by providing the right information at the right time. But it can also emphasize creativity.
In the last year, AI has been deeply applied to the design world, providing the designers with new graphics generated by giving as input a series of selected images and visual design ideas.
Consider the case of Ferrero, which tested the “Deep Dream” technique for Nutella labels. The algorithm exploits a Neural Network for finding and enhancing any possible pattern found in the pictures given as input (it’s called Deep Dream because of the over-repetition of certain abstract patterns that could have hallucinogenic effects). Nutella just combined lines, shapes, polka dots, and zigzags, obtaining the following graphics:
These algorithms free the designer to concentrate on deploying uniquely human strengths: professional judgment and aesthetic sensibilities.
Besides designers, AI can provide all employees with tailored information and guidance, helping them to make better decisions.
These human-like machines can do a lot more beyond our imagination and our planet earth. Recently NASA has revealed its next-generation spacesuit for the next moon mission in 2024. As you might guess, designing a revolutionary spacesuit could be challenging because the project would require dealing with contesting needs. And even more, designers and stakeholders could not go weeks discussing the perfect shape of each widget. That’s where AI comes in: a generative adversarial system allows, through a game-like approach, two AI to compete between themselves to invent the most enhanced outcome. They create numerous designs, join them, and then select the best one and redo.
In this way, AI challenges your vision and preferences, providing new perspectives that you did not see earlier.
Nowadays, the vast amount of AI applications requires dealing with bias and ethical issues. In the previous article, I introduced the democratization of AI: people can test and evaluate the decisions an AI should take to create a sort of ethical database. Because missing the point can expose companies to reputational, regulatory, and legal risks.
Most companies deal with data and AI ethics through ad-hoc discussions on a per-product basis. With no transparent protocol in place on how to identify, evaluate, and mitigate the risks, teams end up either overlooking risks or crossing their fingers in the hope that the problem will resolve by itself. These problems grow by orders of magnitude when you include third-parties, who may or may not be thinking about these questions at all or not share your vision.
However, AI ethics does not come in a ready-to-use box. Given the varying values of companies across dozens of industries, data and AI ethics programs must be tailored to the specific business and regulatory needs relevant to the company.
In conclusion, to set a crystal clear roadmap for AI, it is necessary to build organizational awareness. Not just at teams or company level, but the whole sector and industry must focus on the same issues and build a strong ethic around AI solutions. In this way, companies and governments can formally and informally incentivize employees and people to play a role in identifying AI ethical risks and opportunities. By doing that, organizations assure to face problems with a shared understanding. That is what clients, employees, and all stakeholders are looking for: trustworthiness.
What do you think about AI?
Would you like it to be employed in your company and your daily task?
