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How Machine Learning is Beneficial to the Police Departments?

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It is important to understand the basic nature of machines like computers in order to understand what machine learning is. Computers are devices that follow instructions, and machine learning brings in an interesting outlook, where a computer can learn from the experience without the need for programming. Machine learning transports computers to another level where they can learn intuitively in a similar manner as humans. It has several applications, including virtual assistants, predictive traffic systems, surveillance systems, face recognition, spam, malware filtering, fraud detection, and so on. The police can utilize machine learning effectively to resolve the challenges that they face.


A perspective on the history of Artificial Intelligence (AI)

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Artificial Intelligence (AI) history consists of original work and research by not only mathematicians and computer scientists, but studies by psychologists, physicists, and economists have also been much used. The timeline consists of the pre-1950 era of statistical methods to present AlphaZero in 2017 and more. The most significant push in the development of technology was during the 2nd world war where both the allied forces and their enemies worked hard to develop technology which can help them get superiority over others. The timeline started in 1943, work by McCulloch and Pitts on Artificial Neuron gets the recognition of first work on AI. After work done by and McCulloch, Donald Hebb demonstrated rule for modifying connection strings between neurons -- this is called Hebbian learning.


AI Gets the Glory but ML is Quietly Making Fortunes - insideBIGDATA

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When we think about the future of data science, it's easy to get carried away. For a couple of decades now, if you believe the hype, we've been on the verge of a revolution. A revolution in which "Artificial Intelligence" – as a vaguely defined but enormously powerful force – is always on the verge of solving the world's problems. Or at least make data analysis easier. The reality is, of course, more nuanced.


AI Is Harder Than We Think: 4 Key Fallacies in AI Research

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Artificial intelligence has been all over headlines for nearly a decade, as systems have made quick progress in long-standing AI challenges like image recognition, natural language processing, and games. Tech companies have sown machine learning algorithms into search and recommendation engines and facial recognition systems, and OpenAI's GPT-3 and DeepMind's AlphaFold promise even more practical applications, from writing to coding to scientific discoveries. Indeed, we're in the midst of an AI spring, with investment in the technology burgeoning and an overriding sentiment of optimism and possibility towards what it can accomplish and when. This time may feel different than previous AI springs due to the aforementioned practical applications and the proliferation of narrow AI into technologies many of us use every day--like our smartphones, TVs, cars, and vacuum cleaners, to name just a few. But it's also possible that we're riding a wave of short-term progress in AI that will soon become part of the ebb and flow in advancement, funding, and sentiment that has characterized the field since its founding in 1956. AI has fallen short of many predictions made over the last few decades; 2020, for example, was heralded by many as the year self-driving cars would start filling up roads, seamlessly ferrying passengers around as they sat back and enjoyed the ride.


Machine Learning vs. Artificial Intelligence: Which Is the Future of Data Science? - Dataconomy

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When we imagine the future of AI, we may think of the fiction we see in cinema: highly advanced robots that can mimic humans so well as to be indistinguishable from them. It is true that the ability to quickly learn, process, and analyze information to make decisions is a key feature of artificial intelligence. But what most of us have come to know as AI actually belongs to a subdiscipline called machine learning. Artificial intelligence has become a catch-all term for several algorithmic fields of mathematics and computer science. There are some key differences between them that are important to understand to maximize their advancement potential.


Introduction to NLP Techniques

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Data Scientists work with tons of data, and many times that data includes natural languages like text and speech. That text is usually quite similar to the natural language that we use in our day-to-day life. In this blog, we are going to see some common NLP techniques, with the help of which we can begin performing analysis and building models from textual data. So, let's start with a formal definition… There are various use cases of NLP in our day-to-day life. Computers are great at working with structured data like spreadsheets and database tables, but the problem is we humans usually communicate in words, not in tables.


Algorithmic Architecture: Using A.I. to Design Buildings

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Architecture designed and built in 1921 won't look the same as a building from 1971 or from 2021. Trends change, materials evolve, and issues like sustainability gain importance, among other factors. But what if this evolution wasn't just about the types of buildings architects design, but was, in fact, key to how they design? While designers have long since used tools like Computer Aided Design (CAD) to help conceptualize projects, proponents of generative design want to go several steps further. They want to use algorithms that mimic evolutionary processes inside a computer to help design buildings from the ground up.


Why AI Is Harder Than We Think

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How many of you had a decent conversation with a chatbot? Today we are going to look at the paper "Why AI is harder than you think" published by Melanie Mitchell of Santa Fe Institute. Let's define two words used in the paper: This paper argues that the cycles of AI spring and AI winter come about by people making too overconfident predictions and then everything breaks down. Mitchell has provided examples of times where people make overconfident predictions and outlined four fallacies that researchers make. I found this paper interesting and sharing it here with you.


Nano flashlight enables new applications of light

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In work that could someday turn cell phones into sensors capable of detecting viruses and other minuscule objects, MIT researchers have built a powerful nanoscale flashlight on a chip. Their approach to designing the tiny light beam on a chip could also be used to create a variety of other nano flashlights with different beam characteristics for different applications. Think of a wide spotlight versus a beam of light focused on a single point. For many decades, scientists have used light to identify a material by observing how that light interacts with the material. They do so by essentially shining a beam of light on the material, then analyzing that light after it passes through the material.


Top 10 Artificial Intelligence Innovation Trends to Watch Out For in 2021

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Although the COVID-19 pandemic affected many areas of industry, it did not lessen the impact of Artificial Intelligence in their daily lives. Thus, we can assume that AI-powered solutions will undoubtedly become more widely used in 2021 and beyond. Knowledge will become more available in the coming years, putting digital data at higher risk of being hacked and vulnerable to hacking and phishing attempts. AI and new technologies will help the security service in combating malicious activities in all areas. With strengthened safety initiatives, AI can help prevent cybercrime in the future.