AI is a powerful force that is rapidly and dramatically altering how we work and live. As with any widespread change, the public is concerned about the known and unknown consequences of these complex technologies. This fear leads (sometimes correctly) to a focus on the potential negative consequences of AI, such as worker displacement, as well as broader ethical questions raised by AI applications. Because of the magnitude of the transition to an AI world, it is critical to comprehend the relationship between humans and AI. How we interact with technology and how technology interacts with us to assist and augment abilities such as creativity and intuitive problem-solving is critical to realizing humanity’s full potential. In this blog, we have discussed how collaborative AI can improve human skills.
AI has the potential to free humans from mundane, repetitive, error-prone, and high-risk tasks. This not only frees the individual to perform duties that are more entertaining to them and profitable for their employers, but it also aids in lowering costs by eliminating high-resource, low-margin business operations. Accounting, manufacturing, and surgery are just a few of the tasks that AI can help with.
In many cases, the most effective combination of AI and human skills will reflect humans’ strengths, with AI filling the gap. AI can help us be more accurate in our daily work by combining highly calibrated measurement systems with machine learning and object recognition. We will be able to identify vital information in our field of view that we would have missed otherwise.
This visual enhancement will have a significant impact in life-and-death situations such as surgery. Super-resolution sub-pixel processing for a magnified view of human tissue, for example, will be invaluable to patient safety and physician success. This AI-enhanced image magnification enables more precise incisions to remove more cancer cells while protecting healthy tissue, allowing for less invasive treatment of more cancers.
AI will increasingly improve fundamental human skills, allowing us to be more creative. It will augment pattern recognition, observation, and combining information in novel ways. AI systems are being trained to help with an increasing number of tasks based on growing datasets that are fed into so-called “deep learning” systems. Over time, the computerized parameters will mimic human activity at various stages of the creative workflow in order to automate, accelerate, and strengthen it.
The type of creativity at hand is not limited to that which produces famous works of art. It also includes the creativity that people use every day to see problems and their solutions in new ways. It also promotes better planning and predictive problem-solving, assisting in identifying and avoiding roadblocks sooner.
The IP industry is already employing automation tools to deal with problems creatively in real time by systemizing creativity and automating innovation workflows with XLSCOUT. Users can enhance their comprehension of advanced technological and scientific concepts by comparing them to semantically similar invention descriptions. They can use a weighted semantic system to compare multiple datasets to determine the relative similarity of technological ideas. Selecting semantic language and determining whether it is “less like” or “more like” helps direct the appropriate technology to evaluate ideas and identify competitors.
AI-driven decision making has the potential to transform business by assisting in avoiding the biases and missing information that plague human decision-making. AI has the potential to simplify complex scenarios with multiple, interconnected variables. With sufficiently large datasets and well-designed algorithms, AI can support, assist, or supplant human decision-making in situations where chaotic scenarios, such as market collapse or a natural disaster, can paralyze human decision-makers.
Open-source datasets and white-box (rather than “black”) algorithms will make it simple to experiment with decision-making models. Multi-step decision-making can be set up in real-world simulations with interconnected variables. These simulations could be used in conjunction with process mining to reverse engineer workflow processes to support future human responsiveness.
With AI-based decision augmentation tools, XLSCOUT aids decision-making. It uses comprehensive datasets to generate concise market intelligence metrics that predict monetization potential, litigation risk, and overall patent quality.