As the oracles of Silicon Valley debate whether the latest tech boom is sliding toward bust, there is already talk about what will drive the industry’s next growth spurt. AI, or artificial intelligence, is becoming the next big step in human evolution and it’s moving faster than we planned. Artificial intelligence is a piece of software that can make a complex, human-like decision. First, you need to distinguish between machine learning and AI. Machine learning is a technical exercise. When a statistician or a data scientist or an engineer trains a model to answer a question — that’s machine learning. It’s about finding ways to make human-like decisions using a computer algorithm.
Artificial intelligence, on the other hand, is a more complex engineered process. Artificial intelligence is also more cross-disciplinary than ML. It requires design; it requires infrastructure, and it requires immaculate, predictable data to pull it off correctly. Machine learning is a component of artificial intelligence. But when you talk to your phone, you’re talking to an AI. You’re interacting with a heavily engineered system with many different parts. At the very bottom of that system is a machine learning algorithm that listens to what you say, parses what you say, and then another machine learning algorithm that decides what to do with that information.
The main reason behind this popular field of technology is because of how capable it is in disrupting almost every field in human society, from healthcare to manufacturing to politics. In the agriculture sector, autonomous tractors and AI-based drones monitoring are used to enhance the productivity and crop yield of farmlands. Furthermore, robots and automated machines are also used in these fields to monitor crop health conditions and harvesting. Autonomous Vehicles or Self-driving Cars are the other examples of artificial intelligence, fully integrated into such a system to make the machine work automatically while understanding the nearby surroundings and real-world scenario. E-commerce backed automated warehousing and supply chain management is reducing the manpower and helping storage companies to manage the huge amount of stock or inventory with proper management and supply system. Similarly, artificial intelligence in the healthcare sector is playing a vital role in empowering the machines to diagnose, analyze and predict the various types of diseases, and monitor the patient’s health conditions.
Bias in Artificial Intelligence has been a popular topic over the last few years as artificial intelligence solutions have become more ingrained in our daily lives. Bias is considered to be a disproportionate inclination or prejudice for or against an idea or thing. Bias is often thought of in a human context, but it can exist in many different fields: Statistics — For example, the systematic distortion of an information. It can also be found in research when there is a bias towards the publication of certain experimental significant results. Lastly, it can be seen in social sciences when there is prejudice against certain groups of people. It’s easy to conflate artificial intelligence with superior intelligence, as though machine learning based on massive data sets leads to inherently better decision-making. The problem, of course, is that human choices undergird every aspect of artificial intelligence, from the curation of data sets to the weighting of variables. Usually, there’s little or no transparency for the end-user, meaning resulting biases are next to impossible to account for. Given that artificial intelligence is now involved in everything from jurisprudence to lending, it’s massively important for the future of our increasingly data-driven society that the issue of bias in artificial intelligence be taken seriously.
Feature Image: BENJAMIN M./DOGTOWN MEDIA