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Similarly to how the Web changed how we communicate and collaborate in the 90s, and handheld devices transformed the role of computers in our lives at the beginning of the millennium, Artificial Intelligence is now changing the way we live and work.

AI seen as a brain
Augemented Intelligence - Image by Ahmed Gad

This transformation has already begun; like any paradigm shift, there are few challenges to overcome and new opportunities that can be a catalyst for completing the transition from early adoption to maturity.

The first obvious opportunity is the positive impact AI will have on our society. AI is already increasing humanity ability to do science in adverse and hostile environments(Mars rovers are a timely example). On a day-to-day basis, it has started help elevate our work quality, reduce people exposure to dangerous jobs, and hugely alleviate the burden of repetitive tasks. It will, ultimately, be a decision aid, giving a more in-depth understanding of the context and support in critical decision making.

The second challenge is ethic and data privacy. Bias in models training is an area of active debate and something that requires extreme consideration. Data privacy could be a concern, especially in countries with relaxed data protection policies. Because training AI models need a significant amount of data, the same countries also have an unfair advantage with significant privacy risk associated with it. AI can be powerful, and as any powerful tool, it could be used maliciously. From an ethical standpoint, the industry will need to agree with clarity on which activities would require a degree of higher scrutiny before implementing AI. Technology should be a force of positive change, and the industry cannot underestimate these problems.

Simultaneously, unique opportunities are opening up from the AI ecosystem to answer ethical and data questions. For example, the emerging synthetic data industry will benefit from AI’s mass adoption, helping manage privacy-related risks. AI marketplaces where relevant models and capabilities are available will emerge.

These teething problems are somewhat necessary and welcome. The ongoing debate about how to frame AI within society is pushing a better understanding of the potentials and what needs to be managed better, a fundamental dynamic to support the transition from early adopter to the mainstream.