In a 1980 speech, Steve Jobs referred to computers as “bicycles for the mind.” This idea was inspired by a chart that illustrated how efficient different animals were at moving their bodies. It showed that humans were mediocre in terms of efficiency but amazingly effective in creating art.
In my work with communities impacted by social media and technology, I often see people struggling to make connections. They are often overwhelmed by anxieties and aversions, relationship messes, and the rigidity of their own expectations. These things can jolt you off of your breath bicycle.
A true bicycle of the mind is a vision that allows us to connect, care, and build new relationships. It’s a vision that can be implemented if society recognizes and embraces its power to transform our collective reality. This vision has the potential to shape our physical and digitally enabled spaces as well, bringing us together in shared experiences rather than driving us apart. This can help reconnect humanity and rebuild our social fabric. It can also help us take back our autonomy from the forces of technology that seek to control our lives.
AI Assists Human
The AI industry has made many innovations over the years. For example, AI systems can read MRI scans to identify malignant tumors at an exponentially faster rate than a human would. It can also assist a blind person navigate their surroundings with voice-assisted technology. It can even make it possible to reorder a product that’s been used up.
However, these technologies can come at a cost. While the tech might be impressive to an employer, it’s not always the best for workers. In fact, it may increase safety risks in the digitalized workplace, with the potential to cause the dreaded double burden of work for women online workers or worse, lead to worker sex discrimination and harassment (Huws, 2015; Degryse, 2016). This is especially true for those who are lucky enough to have an employer willing to invest in AI.
ChatGPT and Generative Language
ChatGPT and generative language models like it are changing the world. Initially designed for education and social media applications, they’ve quickly found their way into many business environments.
This technology is also useful in healthcare, law, and other fields. For example, it can be used to create summaries of long articles or complex concepts. It can be a valuable resource for researchers, especially when they’re not familiar with the subject.
However, there are some concerns about generating text that may be misleading, inaccurate, or even nonsensical. This is because a generative language model doesn’t understand the meaning of the text it generates, and so it can’t verify the accuracy of its output.
For example, a chatbot that says it can make you tiffin in ten seconds isn’t true – the word “tiffin” isn’t in the dictionary. It also won’t be able to understand the nuance of what you’re asking it about.
Fortunately, there are ways to avoid some of the more dangerous issues that can arise from relying on a generative language model like ChatGPT for important tasks in the enterprise. For example, if a generative language model generates answers to critical questions that aren’t faithful to the input data, it could lead to wasted time and resources, misguided troubleshooting, and even equipment damage.
Another issue that’s not always immediately obvious is the biases of generative language models when they’re trained on a large amount of data. For example, a generative language model may be trained on books and articles that contain biased information or inaccurate facts.
This isn’t just a concern for users of chatbots, but also for the companies that employ these systems. For example, if a system like ChatGPT produces an answer to a question that isn’t accurate or representative of the information on the web, it could cause legal, financial, and reputational damage to the company that relies on it. And if a system like ChatGPT can be misused to spread fake news or disinformation, it could put lives at risk.
Generative AI and the Patent Industry
Tools such as ChatGPT can be used in several ways in patent legal work, including:
- Prior art searches: Prior art searches are an essential part of the patent application process. They involve searching for existing inventions and other information that may be relevant to the patentability of a new invention. ChatGPT can be used to generate search queries and to identify potentially relevant prior art documents based on keywords, patent classifications, and other criteria.
- Drafting patent applications: ChatGPT can be used to draft patent applications by generating descriptions of the invention, identifying potential claims, and providing guidance on patent language and terminology. However, it is important to note that a human lawyer should review and edit the application to ensure that it meets legal and technical requirements.
- Patentability analysis: ChatGPT can be used to analyze the patentability of an invention by reviewing prior art and comparing it to the invention to determine whether it is novel and non-obvious. ChatGPT can also generate potential claim language and suggest strategies for overcoming potential rejections.
- Patent portfolio management: ChatGPT can be used to manage a portfolio of patents by generating reports on the status of existing patents, identifying potential licensing or infringement opportunities, and providing guidance on filing new applications based on existing patents.
It is important to note that while ChatGPT can be a useful tool in patent legal work, it should be used in conjunction with human expertise and judgment. Patent law is complex, and it is essential to have a deep understanding of legal and technical requirements to ensure that patent applications are effective and enforceable.
Human in the Driving Chair
The human in the driver’s seat is a safe bet for the foreseeable future, but let’s not forget about the unsung heroes in the back seat. In the grand scheme of things, there are three pillars to the human-robot partnership: safety, performance, and convenience. Keeping the humans in the loop will require a lot of hard work, a lot of smarts, and some elbow grease. For starters, we have to find a way to make humans feel at home again.
The nature of work is rapidly changing, driven by the acceleration of connectivity and cognitive technologies. As this trend gathers speed, organizations are reconsidering how they design jobs, organize work, and plan for future growth.
With an augmented workforce, businesses can augment human labor with robotics and artificial intelligence to drive efficiency. This will boost employee satisfaction and productivity.