A new vision for artificial intelligence is using smaller more relevant data sets for dynamic learning generating more effective outcomes and better predictions. This model uses cognitive architecture, learns, transfers learning, and retains knowledge – enabling more valuable and compelling artificial intelligence applications. This approach is more closely related to the brain’s actual structures and much more effective than “neural networks,” which is a catchy name but the similarity to the brain’s actual functioning is in name only. Real advancement in artificial intelligence must live in reality, not theoretical marketing. The current state of artificial intelligence shows the shortcomings of big data and trial-and-error approaches. A new AI vision can be a more effective solution. Smaller data sets, more relevant information, dynamic data, and algorithms will lead to more appropriate outcomes, better tools, and more effective applications.
The rewards for innovative success have become enormous and unpalatable, especially among the five technological giants (Amazon, Apple, Facebook, Google, and Microsoft) forcing these firms to spend absurd amounts of money on lobbying in Washington DC. It’s an expensive and wasteful distraction, but essential in this brave new world. If nothing else, it clogs innovation. It is to our detriment – and the world is literally burning while politicians fiddle – and even more disastrously – impede innovative activity. Applying friction to free thinking and new ideas never ends well.
Let the data tell the story. Remove human bias. Intuitive investment ideas may seem compelling, but more often, these ideas are time-consuming, inefficient, and inferior.
Diverse thinking, innovative approaches, and a willingness to be wrong and start over typically bring superior results.
Trust the model. Data, discipline, and rigor win more often.
Distinguishing what’s happening in the market and the direction of important market metrics – the signal – from garbled, inconsistent, and mostly useless data – the noise – is extremely challenging today. Information is contradictory and transient making data and critical events more confusing and indistinguishable. Unusual circumstances brought about by the pandemic, subsequent supply chain interruptions, inconsistent production and demand, and unclear economic forecasts combined for almost unprecedented uncertainty and unpredictability.
Typically, near-term predictions are reasonable and reliable because we have immediately available and fairly accurate data making short-term predictions reasonably accurate. In other words, we can estimate what will happen because we have a good idea what just happened. But this is not the case today. Predictions based on the near-term past are more muddled now than ever. While we used to be able to say we can see a trend, whether that’s inflation, economic growth, or some other important metric, too much volatility, irrelevance, and lack of applicability (after all, who is going to project from a base that includes a pandemic impacting global supply chains and production?), we really can’t reasonably rely on any of that data to try to find a trend or connect the dots generating a near-term forecast with any meaningful depth of data and understanding
More intense volatility occurring more often will be characteristic of this market from now on. An investment strategy must withstand and profit from this. The only clear signal from the market is that there is far too much noise and not enough of a clear signal. Without clarity, determining an investment strategy is flying blind with no instruments.
Core holdings combined with an ability to withstand and profit from volatility and unpredictability are essential for investors today.
Medical Intelligence is a new discipline, converging human and artificial intelligence. Artificial intelligence will not replace human intelligence, especially in medicine. Diagnosis and treatment will remain a human endeavor. But AI will be an indispensable tool helping human intelligence effectively deliver better quality healthcare.