Data isn’t the only thing holding back artificial intelligence. Factors like deciding where and how to deploy AI in business and public perception also play a role in the future of AI.
Challenges in Artificial Intelligence
- There’s still work to be done before achieving true intelligence. The gap between artificial intelligence and artificial general intelligence is wide. AI must first “learn to learn” in order to understand and perform intellectual tasks that can be done by humans. (MIT Technology Review)
- AI doesn’t always live up to its expectations. With over 50% of people getting their information on AI from movies and TV or social media, the high expectations of AI don’t live up to the hype surrounding it. (McKinsey Global Institute)
- Public opinion of AI is tentative. Public opinion regarding the trustworthiness of AI is tentative. A recent survey found that 50% of consumers feel “optimistic and informed” about AI, while the other half feel “fearful and uninformed”. (Blumberg Capital)
- False positives and bias in data cause uncertainty. Datasets are curated based on human logic and values, making it difficult to completely rule out bias in the resulting ML model. (Sama)
- Shortage of data and lack of infrastructure impede the AI pipeline. More companies are considering AI projects, but few have a process to bring projects to production. Enterprises lack the data and infrastructure to support smooth data flow from ingestion to algorithm, making it difficult to operationalize ML models and intelligence. (IDC)
- Labeling datasets is arguably the hardest part of building AI. Among other AI challenges like having a clear strategy to source the data that AI requires, organizations argue that the hardest part of building AI products is data preparation and labeling. (Inside Big Data)
- Difficulty explaining why a complex decision was reached. AI is programmed to learn by itself, and sometimes, it reaches decisions that no human can explain. We’ve seen this with Google DeepMind’s AlphaZero algorithm, and initiatives like Explainable AI have developed to find answers to this problem. (Intercepting Horizons)
- Fear that AI will negate the necessity of humans in the workforce. There’s a prevailing fear that AI will take over jobs previously performed by humans. However, AI has its limitations. The Future of Jobs report predicts how the division tasks between human and machine will shift between now and 2022. (World Economic Forum)
- Good talent is hard to find. Finding professionals that have the right skillset and experience for AI work is among the top challenges for enterprises who have piloted or embedded AI in their organization. (McKinsey Global Institute)
As new developments in AI occur, the societal and data challenges surrounding the technology will continue to shift and evolve. These challenges should not be overlooked, but rather carefully observed as we pursue human-level intelligence for AI.