Third-wave AI is supposed to be truly intelligent and most similar to human thinking, meaning it will make sense of the independent world and its varying contexts.
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Many might not realize it but artificial intelligence (AI) has ubiquitously made its way into our daily lives. For instance, digital assistants such as Amazon Alexa and Apple Siri handle everyday tasks including scheduling reminders or playing your favorite morning jam. Support channels are being taken over by chatbots and you’re none the wiser.
Impressive as these applications may be, AI is entering its so-called third wave, which is set to create more interesting applications for the technology itself, as reported by Top500.org. Third-wave AI is supposed to be truly intelligent and most similar to human thinking, meaning it will make sense of the independent world and its varying contexts.
Related: How Artificial Intelligence Could Help You Design a Better User Experience
As an example, in this third-wave AI ecosystem, Mind.AI seeks to deliver a truly intelligent AI, with comparable mental intelligence to that of a human. Aside from being able to learn autonomously, Mind’s AI can generalize and even reason abstractly through natural language. Instead of using vast amounts of computational power, Mind aims to build its AI through patented proprietary data structures called “canonicals.” This enables the AI to construct models of how the world works, meaning it’s possible for the AI to perform context-specific tasks with a high degree of success.
While scientists and researchers are pretty excited by these developments, many entrepreneurs wonder how they can leverage the tech to their advantage. Fortunately, AI platforms and AI-driven cloud-based services are now working on making advanced capabilities available to ordinary users and smaller enterprises. Here are three ways third-wave AI can benefit them.
1. Better insights
Third-wave AI is capable of searching for patterns in large volumes of data and even across data sets. This could yield connections that have not been linked previously, resulting in new insights that could guide businesses in their decision-making.
Various AI-driven data and analytics tools are now available for use even by ordinary users. Growthbot, for example, compiles and analyzes marketing data and research such as search engine optimization (SEO) keywords, competitor intelligence and prospect leads. This way, you only have to ask its chatbot questions in order to get timely insights about your business and even your competitors.
Individuals can also leverage large amounts of communication data to gain insights about their professional and personal connections. Trove is a cloud-based AI-driven tool that helps users make sense of their email and social networks. Trove finds patterns in conversations and makes them searchable. This provides users with an accurate view of their relationships, helping them to better navigate their social connections.
AI can even be used to generate and analyze synthetic data (synthetic data is information that’s artificially manufactured rather than generated by real-world events), in order to make improvements in a variety of applications. Nueromation helps projects use synthetic data to help AI learn and improve its algorithms. Among its solutions are AI-powered cameras that feature superior facial or feature recognition capabilities. Accurate detection and classification could benefit projects working on security, retail or even self-driving vehicles.
Related: How to Make AI-Driven Emails Compelling Without Being Creepy
2. More effective automation
AI can also be used to automate just about any activity. Take personal finance. Micro-investing app Mylo uses AI to help ordinary users start an investment portfolio without any fuss. The app takes your spare change from purchases and invests the money in a diversified portfolio tailored to your preferences.
In the context of business, AI-driven automation tools are now being used in specific and specialized areas of business such as finance, manufacturing and task management. Roger, for instance, can automate the accounts payable flow, which is a mundane yet critical part of the business. Users simply have to scan bills and the system sends it through a streamlined process to ensure that bills are paid on time and reconciled with the books.
Third-wave AI could even bring further innovation to areas that are already benefiting from automation such as manufacturing. Manufacturing has largely been linear. But with third-wave AI, it might even be possible for processes to take on more dynamic and agile methods where systems would be able to work through sprints and iterations rather than in the conventional linear fashion. Hitachi already uses AI to automatically adjust equipment settings in every production run for greater efficiency, as reported by TechTarget.
3. Smarter assistants
Siri and Alexa might work wonders, but they’re still liable to trip even with some basic instructions. It’s because of these limitations that many of us still don’t pass on more serious tasks or decisions to AI.
Unlike Siri and Alexa, which only respond to one instruction at a time, new AI assistants such as Aigo are adaptive. They can remember and learn from your preferences so that they will be able to sustain meaningful conversations with users as if you are chatting with an actual person that knows you.
As a development platform, Aigo could be enhanced to deliver more specialized interactions allowing it to be used beyond personal assistance such as education and healthcare. It can also be adapted for use in enterprise and business applications similar to other AI-driven tools.
Related: The Real-World Applications of Artificial Intelligence in Marketing
AI’s (current) limitations
The third wave of AI is still ongoing and there are some kinks that need to be ironed out for it to meet our evolving expectations.
One example of the hurdles is called the AI black box dilemma, as explained in this piece by Sentient. Essentially, the AI’s decision-making process can become so convoluted that tracing how AI came to a decision can be impossible. A business using deep learning to improve its app design might be given the ideal solution by the AI. However, the business would still need to know the reason why the machine came to such a conclusion. Human decision-makers have the need to justify their decisions. Fortunately, a possible solution is to use blockchain’s transparent record keeping to keep track of AI’s “thought process” and make AI accountable for its decisions.
Instead of having these issues become knocks against the technology, these should be considered potential areas for improvement. In fact, it’s quite exciting that ordinary users and businesses could tap into these cutting-edge technologies. My recommendation is to subscribe to the various cloud and blockchain-based applications and freely experiment with them. You may even find that you can outsource the work to your new AI assistants.
