Unveiling the Future of AI: Shaping Tomorrow's Possibilities

"Future of AI." From sci-fi dreams to tangible reality, artificial intelligence reshapes industries, societies, and how we perceive technology.

Let's try to understand the concept from the movies we saw in the past.

Transcendence (2014)

Dr. Will Caster (Johnny Depp) is a scientist who researches the nature of sentience, including artificial intelligence. He and his team work to create a sentient computer; he predicts that such a computer will create a technological singularity, or in his words, "Transcendence."

Her (2013)

A lonely writer develops an unlikely relationship with an operating system designed to meet his every need.

Many more movies are in the library, but these two titles have become realistic. DALL-E, Bard, Copy.ai, Chatbot, and many more.

Gartner defines artificial intelligence (AI) as applying advanced analysis and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions and take action. This definition is consistent with the current and emerging state of AI technologies and capabilities, and it acknowledges that AI now generally involves probabilistic analysis (combining probability and logic to assign a value to uncertainty).

What are the main emerging AI techniques?

The essential emerging techniques, in descending order of maturity, are:

• Natural language processing (NLP). NLP provides intuitive forms of communication between humans and systems. NLP includes computational linguistic techniques (symbolic and subsymbolic) aimed at recognizing, parsing, interpreting, automatically tagging, translating and generating (or summarizing) natural languages.?

• Knowledge representation. Capabilities such as knowledge graphs or semantic networks aim to facilitate and accelerate access to and analysis of data networks and graphs. Through their representations of knowledge, these mechanisms tend to be more intuitive for specific types of problems. Adoption of knowledge graph techniques has accelerated quickly over the last three years.

• Agent-based computing. This is the least mature of the established AI techniques, but it is quickly gaining in popularity. Software agents are persistent, autonomous, goal-oriented programs that act on behalf of users or other programs. Chatbots, for example, are increasingly popular agents.

Two main classes of agent applications are commonly used with existing solutions today:

• Task automation agents can be generic (e.g., meeting scheduling assistants in email systems) or more specific (e.g., contract validation softbots for sales automation applications).
• Autonomous object programs can serve functions such as automatic temperature-setting (e.g., found in car diagnostic systems or home thermostats).

What's new in AI 2023?

The latest annual McKinsey Global Survey?on the current state of AI confirms the explosive growth of generative AI (gen AI) tools. Less than a year after many of these tools debuted, one-third of our survey respondents say their organizations are using gen AI regularly in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work, and more than one-quarter of respondents from companies using AI say gen AI is already on their boards’ agendas.

Two types of GenAI innovations dominate

Generative AI is dominating discussions on AI, having increased productivity for developers and knowledge workers in very real ways, using systems like ChatGPT. This has caused organizations and industries to rethink their business processes and the value of human resources, pushing GenAI to the Peak of Inflated Expectations on the Hype Cycle.

Gartner now sees two sides to the generative AI movement on the path toward more powerful AI systems:

Innovations that will be fueled by generative AI

• Artificial general intelligence (AGI) is the (currently hypothetical) intelligence of a machine that can accomplish any intellectual task that a human can perform.
• AI engineering is foundational for enterprise delivery of AI solutions at scale. The discipline creates coherent enterprise development, delivery, and operational AI-based systems.
• Autonomic systems are self-managing physical or software systems performing domain-bounded tasks that exhibit three fundamental characteristics: autonomy, learning and agency.?
• Cloud AI services provide AI model building tools, APIs for prebuilt services and associated middleware that enable the building/training, deployment and consumption of machine learning (ML) models running on prebuilt infrastructure as cloud services.
• Composite AI refers to the combined application (or fusion) of different AI techniques to improve the efficiency of learning to broaden the level of knowledge representations. It solves a wider range of business problems in a more effective manner.
• Computer vision is a set of technologies that involves capturing, processing and analyzing real-world images and videos to extract meaningful, contextual information from the physical world.
• Data-centric AI is an approach that focuses on enhancing and enriching training data to drive better AI outcomes. Data-centric AI also addresses data quality, privacy and scalability.
• Edge AI refers to the use of AI techniques embedded in non-IT products, IoT endpoints, gateways and edge servers. It spans use cases for consumer, commercial and industrial applications, such as autonomous vehicles, enhanced capabilities of medical diagnostics and streaming video analytics.
• Intelligent applications utilize learned adaptation to respond autonomously to people and machines.
• Model operationalization (ModelOps) is primarily focused on the end-to-end governance and life cycle management of advanced analytics, AI and decision models.?
• Operational AI systems (OAISys) enable orchestration, automation and scaling of production-ready and enterprise-grade AI, comprising ML, DNNs and Generative AI.
• Prompt engineering is the discipline of providing inputs, in the form of text or images, to generative AI models to specify and confine the set of responses the model can produce.?
• Smart robots are AI-powered, often mobile, machines designed to autonomously execute one or more physical tasks.
• Synthetic data is a class of data that is artificially generated rather than obtained from direct observations of the real world.

Innovations that will fuel generative AI advancement

• AI simulation is the combined application of AI and simulation technologies to jointly develop AI agents and the simulated environments in which they can be trained, tested and sometimes deployed.
• AI trust, risk and security management (AI TRiSM) ensures AI model governance, trustworthiness, fairness, reliability, robustness, efficacy and data protection.
• Causal AI identifies and utilizes cause-and-effect relationships to go beyond correlation-based predictive models and toward AI systems that can prescribe actions more effectively and act more autonomously.
• Data labeling and annotation (DL&A) is a process where data assets are further classified, segmented, annotated and augmented to enrich data for better analytics and AI projects.
• First-principles AI (FPAI) (aka physics-informed AI) incorporates physical and analog principles, governing laws and domain knowledge into AI models. FPAI extends AI engineering to complex system engineering and model-based systems
• Foundation models are large-parameter models trained on a broad gamut of datasets in a self-supervised manner.
• Knowledge graphs are machine-readable representations of the physical and digital worlds. They include entities (people, companies, digital assets) and their relationships, which adhere to a graph data model.
• Multiagent systems (MAS) is a type of AI system composed of multiple, independent (but interactive) agents, each capable of perceiving their environment and taking actions. Agents can be AI models, software programs, robots and other computational entities.
• Neurosymbolic AI is a form of composite AI that combines machine learning methods and symbolic systems to create more robust and trustworthy AI models. It provides a reasoning infrastructure for solving a wider range of business problems more effectively.
• Responsible AI is an umbrella term for aspects of making appropriate business and ethical choices when adopting AI. It encompasses organizational responsibilities and practices that ensure positive, accountable, and ethical AI development and operation.

Conclusion

We should adopt new technologies for rapid growth. In the future, AI will generate a positive impact on our society. Top tech companies are getting big deals in AI. If we recall computer generation in the '90s, people reacted the same way they do today. Now, the computing generation is way beyond and had a significant impact on our society. AI will generate more jobs, enhance our skillset on the set of tools, and minimize our efforts to be more productive.