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Next Generation A.I.

 

 

Artificial Intelligence has changed the present era of technology. In the next generation the Blue Brain Project may be at the target, which is still undergoing. The brain which can feel the emotions. Next generation A.I will control human beings by reading human minds.     

 The current state of AI can do many things, but there are still challenges to workout before we reach human-like brains. Some of the current challenges with AI include learning without labels and generalization of learning. What is the edge and the future of AI? For businesses to adopt cutting-edge tech can be an advantage.

As business adoption of artificial intelligence (AI) expands rapidly, so does the vocabulary used to describe the technology and the myriad ways companies are putting it to work. While terms such as algorithm, machine learning and neural networks have become as familiar today as cloud, SaaS and IoT, dozens of new AI terms and trends are already entering the field or rising in importance. Here’s a look at some of those—and why you should become familiar with each. 

 


AI architect

A data scientist who takes a direct role in applying artificial intelligence to improve business processes. AI architects look for applications of AI for the company as a whole, such as automating recruiting and hiring, as well as for ways to put AI to work automating routine work (like developing chatbots for customer service)

 

Algorithmic auditing

 Also known as shadow auditing. The process is used to identify AI “blind spots.” As concerns grow about the hidden bias in AI systems, algorithmic auditing is a way to seek out flaws in structural design, coding and training data sets and to assess the system for consistency, transparency, accuracy and fairness. Such auditing is being used to detect bias in AI tools used to make decisions in financial services, the criminal justice system and hiring.

 

Data curator

 This is a new role that bridges the gap between data scientists and those in the business that consume data-driven insights. Data curators combine an understanding of business objectives with knowledge of data collection, processing and analytics, enabling them to streamline the use of data to solve business problems. As AI becomes more central to the enterprise, data curators are increasingly necessary to make its findings understandable. 

 

 

Edge AI

 The application of edge computing, which processes data on devices at the nodes of a network, to artificial intelligence. With edge AI, data on devices like sensors or smartphones can be used to train machine learning algorithms, enabling faster decision making and real-time responsiveness. By removing the need to connect to cloud-based systems, edge AI eliminates latency delays and decreases data vulnerability and storage costs. Emerging applications include self-driving cars, robots and AI-powered industrial equipment. 

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