If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
I consult and educate companies to transform technology and data into a valuable, measurable, and monetizable business asset. In my data analytics and machine learning (ML) consulting engagements, I often come across use cases aimed at solving scientific problems using data, such as predicting the failure of a turbine or forecasting the carbon footprint of our IT data center. But what exactly is a scientific problem, and how is it different from a data problem? Is it really necessary to validate a known scientific fact or model again with data? Before answering these questions, let's define some key terms and scientific laws needed to answer these questions.
Credit card frauds are a "still growing" problem in the world. Losses in frauds were estimated in more than US$27 billion in 2018 and are still projected to grow significantly for the next years as this article shows. With more and more people using credit cards in their daily routine, also increased the interest of criminals in opportunities to make money from that. The development of new technologies puts both criminals and credit card companies in a constant race to improve their systems and techniques. With that amount of money at stake, Machine Learning is surely not a new word for credit card companies, which have been investing on that long before it was a trend, to create and optimize models of risk and fraud management.
Before we get into the machine learning (which is what you are all here for, I know), I started with preliminary data exploration. This helps identify machine learning questions, validate conclusions drawn from machine learning, and find basic statistical descriptors of the dataset that cannot be identified through machine learning alone. First, I explored the amount spent per transaction. As expected for anyone who frequents a coffee shop, the vast majority of transactions are below $8. It is interesting that there is a large peak around $1–2, perhaps these are those people who just go in and get a cup of Pike Place, with room.
This article investigates TensorFlow components for building a toolset to make modeling evaluation more efficient. Specifically, TensorFlow Datasets (TFDS) and TensorBoard (TB) can be quite helpful in this task. While completing a highly informative AICamp online class taught by Tyler Elliot Bettilyon (TEB) called Deep Learning for Developers, I got interested in creating a more structured way for machine-learning model builders -- like me as the student -- to understand and evaluate various models and observe their performance when applied to new datasets. Since this particular class focused on TensorFlow (TF), I started to investigate TF components for building a toolset to make this type of modeling evaluation more efficient. In doing so, I learned about two components, TensorFlow Datasets (TFDS) and TensorBoard (TB), that can be quite helpful and this blog post discusses their application in this task.
AI is installed as standard, while maintaining the reliability and functions of the SMARTDAC GX/GP Series Paperless Recorders. With no complicated settings, you simply register the channels that you want to monitor as future pens, draw the near future as waveforms. The GX/GP series is a panel mount or portable paperless recorder that provides intuitive touch panel operation. Its highly flexible modular I/O architecture enables you to acquire, display, and record data such as temperature, voltage, current, flow, and pressure in various industrial production and development sites. Using acquired data to predict future data, draw predicted future waveforms along with real-time data on the trend monitor.
Artificial Intelligence and Machine Learning are awesome. They allow our mobile assistants to understand our voices and book us an Uber. AI and Machine Learning systems recommend us books in Amazon, similar to the ones we've liked in the past. They might even make us have an amazing match in a dating application and meet the love of our life. All of these are cool but potentially harmless applications of AI: If your voice assistant doesn't understand you, you can just open the Uber application and order a car yourself.
Artificial intelligence is one of the most exciting technological improvements to encircle our society in living memory, however few individuals have a solid comprehension of AI as well as the plethora of ways that it is changing our planet. Nowhere is Artificial intelligence more significant and tumultuous than at the energy industry, where professionals from a broad assortment of backgrounds are discovering it immensely beneficial. Nonetheless, the use of AI from the petroleum and gas industry remains largely misunderstood, and lots of prospective entrants into the sector don't have any clue where to start cleaning up with this intricate topic. Here's a breakdown of how AI is disrupting oil and gas, and why intelligent machines will be imperative to the future of the energy sector. When there's a simple way to describe the part of AI from the gas and oil industry, it is that this technology has become an integral part of the way that energy businesses and professionals achieve their aims. Gas and oil companies have been enormous collectors of information; if nicely employees could not access tremendous treasure troves of information about the area they are working in, for example, they'd never have the ability to be successful in their tasks while ensuring workplace safety and cost-effectiveness.
The COVID-19 pandemic is an incredibly complex and rapidly evolving global public health emergency. Facebook is committed to preventing the spread of false and misleading information on our platforms. Misinformation about the disease can evolve as rapidly as the headlines in the news and can be hard to distinguish from legitimate reporting. The same piece of misinformation can appear in slightly different forms, such as as an image modified with a few pixels cropped or augmented with a filter. And these variations can be unintentional or the result of someone's deliberate attempt to avoid detection.
Although the initial wave of the SARS-CoV-2 pandemic has abated in many countries, healthcare providers are still looking to identify as many COVID-19 patients as possible and contain the disease. Fast and accurate diagnosis is especially important when unsuspecting patients with a coronavirus infection come to the hospital with health complaints but don't yet show symptoms of COVID-19. Nasal swab samples analyzed by RT-PCR are currently recommended for the diagnosis of COVID-19, however, supply shortages, a wait time of up to two days for results, and a false negative rate as high as 1 in 5 mean alternative, large-scale COVID-19 screening tools are still being sought. SARS-CoV-2 is known to damage lung tissue, and in a distinct way that doctors are now seeking to exploit for new diagnostic approaches. Many COVID-19 patients develop pneumonia, which can progress to respiratory failure and sometimes death.