Ways AI could evolve: The world of artificial intelligence is only just beginning

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningGiven all the innovations taking place in the AI world, the possibilities that it creates are seemingly endless. Yet with increasingly prevalent concerns about ethics and the responsible use of AI, many business and information technology leaders are looking to better understand how this technology will affect organizations and society, both now and in the future.

Looking at future scenarios for how AI could evolve can help IT leaders demystify this emerging technology and better understand both its possibilities and its limitations. Here are seven possible future AI scenarios, ordered from those that may have an impact in the nearer term to those that will require many years before potentially becoming reality:

Ways AI could evolve: Augmented intelligence: complementing human strengths

Augmented intelligence is the idea of taking human intelligence as a starting point and supporting it through intuitive, yet non-invasive, user interfacing – such as holograms or interactive visualizations.

Examples in practice already exist, including augmented diagnosis for medical specialists or contextual recommendations for call center agents. Further development will require advances in human-machine interfacing, but this innovation will likely have significant business impacts in the near-term.

Generative AI: improving agency and emotion

Generative AI is the ability for machines to produce completely original and realistic structures, designs and even art. The potential of this technology is in already unfolding – being used, for example, to design new materials in engineering or augment development of new protein structures for vaccine production.

However, generative AI also comes with limitations and concerns given its potential for malevolent use, such as creating deepfakes or other fraudulent content. Regulatory or legislative hurdles could also hinder advances in this area, and developers will need to carefully consider AI ethics in the research and design phase.

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Composite AI: going beyond machine learning

Composite AI is the combination of multiple AI techniques, such as machine learning and graph analytics. It is often used to combine data with other knowledge sources, such as human expertise and causal reasoning, enabling the development of a new generation of AI solutions.

For example, a company can use composite AI to build a more accurate predictive maintenance solution, which not only relies on sensor data but also experience-based heuristics or physical engineering models. This scenario is already unfolding, likely to have an impact within three to five years.

Steroid AI: more powerful hardware for more intelligence

The main idea behind steroid AI is improving the capacity of intelligent systems by using increasingly powerful hardware, such as neuromorphic hardware or quantum computing. Such advances will not change AI in any fundamental way; rather, it will allow companies to build AI solutions that are extremely fast and can encapsulate ever more data and knowledge.

For example, a virtual customer assistant may now have the computing capacity to respond to questions about product A and B, but with more powerful hardware, it could also cover products C, D and E. Advances in this area are ongoing, but the timeframe of its impact is uncertain. Neuromorphic computing is projected to have an impact within three to five years, while quantum and other more advanced systems will take longer.

Transcendent intelligence: synergy of humans and AI

Like augmented intelligence, transcendent intelligence takes human intelligence as a starting point, but in this case, it complements human intelligence using invasive human-brain implants.

While some early prototypes of this technology exist, such as artificial limbs or thought-controlled airplanes, these instances will require years of research to work effectively and responsibly. Significant progress is needed in neuroscience and psychology for deeper brain-machine integration, as well as in studying potential ethical and mental health implications. […]

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