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) …
Artificial Intelligence (AI) and machine learning (ML) are gaining increasing traction in today's digital world. Machine learning (ML) is a subset of AI involving the study of computer algorithms that allows computers to learn and grow from experience apart from human intervention. Python has been the go-to choice for Machine Learning and Artificial Intelligence developers for a long time. Python offers some of the best flexibilities and features to developers that not only increase their productivity but the quality of the code as well, not to mention the extensive libraries helping ease the workload. Arthur Samuel said -- "Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed." The NumPy library for Python concentrates on handling extensive multi-dimensional data and the intricate mathematical functions operating on the data.
In supervised machine learning, you usually have an input X, which goes into your prediction function to get your Y . You can then compare your prediction with the true value Y. This gives you your cost which you use to update the parameters θ. Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. So, let's start sentiment analysis using Logistic Regression We will be using the sample twitter data set for this exercise.
Technology is expected to continue changing and getting better. App building technology is particularly focused to grow faster as a result of the increased need for mobile applications with improved user interface (UI) and artificial intelligence (AI). Graphical programming is therefore inevitable as the platforms make app development more seamless and less costly. This is where low code development comes in as leverage especially for the workforce with lesser competency levels in IT.
Cryo-electron microscopy (cryo-EM) allows scientists to produce high-resolution, three-dimensional images of tiny molecules such as proteins. This technique works best for imaging proteins that exist in only one conformation, but MIT researchers have now developed a machine-learning algorithm that helps them identify multiple possible structures that a protein can take. Unlike AI techniques that aim to predict protein structure from sequence data alone, protein structure can also be experimentally determined using cryo-EM, which produces hundreds of thousands, or even millions, of two-dimensional images of protein samples frozen in a thin layer of ice. Computer algorithms then piece together these images, taken from different angles, into a three-dimensional representation of the protein in a process termed reconstruction. In a Nature Methods paper, the MIT researchers report a new AI-based software for reconstructing multiple structures and motions of the imaged protein -- a major goal in the protein science community.
People have long debated what constitutes the ethical use of technology. But with the rise of artificial intelligence, the discussion has intensified as it's now algorithms not humans that are making decisions about how technology is applied. In June 2020, I had a chance to speak with Paula Goldman, Chief Ethical and Humane Use Officer for Salesforce about how companies can develop technology, specifically AI, with ethical use and privacy in mind. I spoke with Goldman during Salesforce's TrailheaDX 2020 virtual developer conference, but we didn't have a chance to air the interview then. I'm glad to bring it to you now, as the discussion about ethics and technology has only intensified as companies and governments around the world use new technologies to address the COVID-19 pandemic. The following is a transcript of the interview, edited for readability. Bill Detwiler: So let's get right to it.
China's tech industry has been hit hard by US trade battles and the economic uncertainties of the pandemic, but it's eager to bounce back in the relatively near future. According to the Wall Street Journal, the country used its annual party meeting to outline a five-year plan for advancing technology that aids "national security and overall development." It will create labs, foster educational programs and otherwise boost research in fields like AI, biotech, semiconductors and quantum computing. The Chinese government added that it would increase spending on basic research (that is, studies of potential breakthroughs) by 10.6 percent in 2021, and would create a 10-year research strategy. China has a number of technological advantages, such as its 5G availability and the sheer volume of AI research it produces.
A label is a variable to be predicted. In this example, I will predict whether the website visitor will make any transactions and I gave this label the name "purchase". This can be derived from the existing variable "totals.transactions". For simplicity, let's make this prediction a black or white situation, either "purchase" or "no purchase". Since the model training cannot handle string value as the output result, therefore it is necessary to code them into numbers.
The volume of data businesses produce today carries much significance in terms of overall growth. Foresighted companies know that if they want to vie in a highly-competitive market, they must deploy advanced analytics to ever-growing data sets. Using business intelligence allows them to look into their historical and current data sets, and it provides them predictive views of their business operations. Augmented by artificial intelligence and machine learning, business intelligence provides enterprises with decision-making context and recommendations. This significantly drives a move towards decision intelligence, the creative blend of technology into enterprise decision-making strategies and workflows.
The reason for focusing on this area of bias is regarded as important by some enterprises. This is because unconscious bias is often easy to miss. Moreover, unconscious bias is often seen to be far more pervasive in the workplace than blatant discrimination. According to some researchers, unconscious bias can be blamed for lower wages, less opportunities for advancement and high turnover. Unconscious biases are types of social stereotypes held by members of one group about other groups of people.
Machine learning is perhaps old hat by now, but what's never going to be old hat is cats. People just can't seem to get enough of them. Learning Factory is an Early Access game that just released last month about building an automated factory that produces the things cats want to buy, then sells them. Your job is to keep the shelves stocked and the cats happy—and earn money by selling at optimal prices. By making offers to cats your factory can train up machine learning models that will then automatically adjust market prices to account for trends and the wallets of the cats in question. Rich cats want fancy expensive cat towers and food, while normal cats just want a good deal on a ball of yarn and construction worker cats want raw materials. It's a near concept that bears out pretty well in action: Do you want to try to make a huge, all-inclusive single machine learning model or instead focus on specific models tailored to each customer type?