- Sort by Default Order
The AI segment is currently very fragmented, characterized with most companies focusing on a silo approaches to solutions. Longer term, Mind Commerce sees many solutions involving multiple AI types as well as integration across other key areas such as the Internet of Things (IoT) and data analytics.
Mind Commerce AI reports focus on solutions to specific problems, AI support of various apps and services, as well as AI integration with other technologies such as Big Data, IoT, and Blockchain. A sampling of Mind Commerce AI reports includes:
- Artificial Intelligence Market by Platforms, Components, Deployment Mode, Applications, and Industry Verticals 2018 – 2023
- Artificial Intelligence Impact on Public Safety, Security and Privacy
- Personal Artificial Intelligence and Robotics Market Outlook and Forecasts 2017 – 2022
- Virtual Personal Assistants: Artificial Intelligence Enabled Smart Advisors and Intelligent Agent Market Outlook and Forecasts 2018 – 2023
- Artificial Intelligence Convergence: AI in Analytics, Communications, Computing, IoT, Public Safety, Robotics, and Security 2017 – 2022
- AI in Internet of Things (IoT), Data Analytics, and Virtual Private Assistants 2017 – 2022
- Artificial Intelligence in Big Data Analytics and IoT: Market for Data Capture, Information and Decision Support Services 2017 – 2022
- Market for Artificial Intelligence in Internet of Things (IoT) Security and Fraud Prevention
- Chatbots and Artificial Intelligence: Market Assessment, Application Analysis, and Forecasts 2017 – 2022
There are many potential use cases for AI within the cybersecurity domain. For example, AI may be used in IoT to bolster security, safeguard assets, and reduce fraud.
There are varying opinions about security in IoT. For example, some companies favor a distributed (decentralized) approach whereas other companies believe a more centralized approach leveraging strictly centralized cloud architecture makes more sense.
Mind Commerce sees no way in which signature based security solutions will work with IoT in an edge computing environment for a variety of reasons including the limitation on throughput of communications between distributed end-points and centralized cloud.
AI is a must for IoT in an edge computing environment
AI has various advantages including the fact that it is a more lightweight application (because it does not require all the data that comes with tracking digital signatures/code for known viruses), more effective in identifying malware, easier and less costly to maintain as there is no need to constantly identify new malware code. This is all because AI based security is looking for malicious behaviors rather than known malicious code.
Longer term, AI will move beyond fraud prevention and prevention of malicious acts as AI will be used to feed advanced analytics and decision making. This will be especially true in IoT solutions involving real-time data as AI will be used to make determinations for autonomous actions.
An example of a Mind Commerce AI report covering cybersecurity is Artificial Intelligence Convergence: AI in Analytics, Communications, Computing, IoT, Public Safety, Robotics, and Security 2017 – 2022
Consumer-facing apps and services supported by AI are many and varied including chatbots and Virtual Personal Assistants (VPA) in support of customer care and lifestyle enhancement. The automobile industry is another example in which, AI is becoming increasingly useful, both in the near-term for solutions such as inclusion of VPAs, and longer term use cases such as AI support of self-driving vehicles.
Another consumer market area in which AI will be integrated is wearable technology. As wearables become more main stream, and integrate into everyday life with increasing dependency, there will be a need for integration with Artificial Intelligence, Big Data, and Analytics.
AI in Enterprise
AI is expected to have a big impact on data management. However, the impact goes well beyond data management as we anticipate that these technologies will increasingly become part of every network, device, application, and service.
One area important to enterprise will be Intelligent Decision Support Systems (IDSS), which are a form of Expert System which utilize AI to optimize decision making. IDSS will be used in many fields including agriculture, medicine, urban development, and other areas. IDSS will also be used in policy making and strategy at the highest levels of enterprise as well as governmental organizations.
Evolution from Weak AI to Strong AI
Weak AI systems and solutions are characterized as those that are narrowly focused on typically one area. The aforementioned VPA technology is a good example. In contrast, Strong AI is getting much closer to sentient beings such as humans, and have traits of self-awareness. In other words, Strong AI is more like human-level intelligence.
AI systems will evolve from monolithic, single-purpose systems to more integrated AI systems that singularly, or in the case of multiple connected systems, collectively represent Strong AI. The use of Strong AI systems to provide orchestration and overall governance of lesser AI modules will enable intelligence that has broader contextual decision making capabilities.
Artificial General Intelligence
While many forms of AI today are oriented towards one type of information or means of gathering and interpreting data, General AI pertains to those systems that have the ability to learn from many different environments. In other words, Artificial General Intelligence performs more like a human being in terms of ability to make use of inputs and data which normal AI would ignore as disparate and seemingly insignificant.
One of the challenges of creating AI which can think like a human is that machines do not behave in the same ways or share the same sense of values. These traits are part of the human condition that has bearing on the decision process. Another challenge is creating machines that have the human ability to make inferences and judgements based on limited information and nuance.
For example, humans can apply experiential learning in ways that computers currently cannot. In addition, machines find certain tasks very difficult that a