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) …
Chromium-based browser maker Brave has launched a beta of its Brave search engine in a bid to create a privacy-focused alternative to Google. The new search engine puts Brave into the category of firms that have both a browser and a search engine: Google, Microsoft, Yandex and Baidu are also among these companies with both. It's hard to fault Google's record on security and patching but privacy is another matter for the online ad giant. Brave acquired the search engine Tailcat in March and promised to take on Google by approaching online search with a greater focus on privacy. Brave said its search is built on top of a completely independent index, and doesn't track users, their searches, or their clicks. "Brave has its own search index for answering common queries privately without reliance on other providers," it said.
Do you know that a full-time working translator can translate approximately 520,000 words per year? There would be no wrong in saying that the translation industry has existed for centuries and will progress in double digits in the upcoming years. Because digital realms continuously push for more shared and globalized experiences, the current worth of the global translation industry is $56.1 billion, and the figure is expected to increase at a swift pace in upcoming years. The number is projected to surpass $70 billion by the year 2023. It's been more than 10 years since the launch of Google translate by utilizing phase-based machine translation algorithms.
How often have you heard "The Machine Learning Application worked well in the lab, but it failed in the field. It is not the fault of the Machine Learning Model! This blog is not yet another blog article (YABA) on DataOps, DevOps, MLOps, or CloudOps. I do not mean to imply xOps is not essential. For example, MLOps is both strategic and tactical. It promises to transform the "ad-hoc" delivery of Machine Learning applications into software engineering best practices. We know the symptoms: Most machine-learning models trained in the lab perform poorly on real-world data [1, 2, 3, 4]. Machine Learning created profits in the year 2020 and will continue to increase profits in the future. However, many problems hold back the progress and success of Machine Learning application rollout to production. I focus on what it is the most significant problem or cause: the quality and quantity of input data in Machine Learning models [1,4]. We realized the quantity of high-quality data was the bottleneck in predictive accuracy when we started showing near, or above, human-level performance in structured data, imagery, game playing, and natural language tasks. How many times do we look at the Machine Learning application lifecycle's conceptualization to realize a Machine Learning model is not at the beginning (Figure 2)? We can research and improve the tools of the Machine Learning application lifecycle. But that only lowers the cost of deployment. Arguably, the Machine Learning model's choice is not a critical part of deploying a Machine Learning application. We have a "good enough" process or pipeline to choose and change the Machine Learning model, given a training input dataset. However, when achieving State-of-the-Art (SOTA) results, the input data seems to have the most significant impact on the output predictive data (Figure 2). We seem to know the cause: input data that was garbage results in garbage output predictive data. New data input to a trained Machine Learning model determines the accuracy of the output. We divide Machine Learning input data into four arbitrary categories, defined by the Machine Learning application output accuracy. GPT-3 is an example . GPT-3 trained with an enormous amount of data . GPT-3 is frozen in time as a transformer that you access through an API. Concept Drift is a change in what to predict. For example, the definition of "what is a spammer." We do not cover Concept Drift here. I do not think of it as a problem but rather as a change in the solution's scope. An example of Case 2: Data Drift, is that Case 1: "It works!, is a temporal phenomenon.
Healthcare seems to be top of the to-do lists of CEOs of tech's biggest companies: Amazon is launching its own healthcare business, Apple's turning the iPhone into a patient engagement and diagnostics tool, while Google's parent company Alphabet is betting heavily on healthcare through its investment arm, AI and analytics. And the other big tech giant isn't getting left behind either: Microsoft has also got big plans. It's been looking at healthcare in the hope that technology could play a role in helping to address some of the health industry's most pressing problems. Here's a look at the best, most advanced fitness trackers for runners, athletes, and pros. "Some of the longest-standing challenges are around disconnectedness of data, disconnectedness of care teams, and frankly disconnectedness of patients to their own care," says Tom McGuinness, corporate VP of global healthcare & life sciences at Microsoft.
Many companies seem eager to leverage artificial intelligence and machine learning capabilities, if for no other reason than to be able to let their employees, customers, and business partners know that they're on the leading edge of technology progress. At the same time, a lot of businesses are looking to enhance the experiences of customers and channel partners, in order to increase brand loyalty, boost sales, and gain market share--among other reasons. Some have found a way to combine these goals, using AI-powered tools to improve the way they deliver products, services, and support to their clients and business partners. G&J Pepsi-Cola Bottlers began its foray into AI and machine learning in January 2020, when it partnered with Microsoft to better understand the AI and machine learning components within Microsoft's Azure cloud platform. With guidance from Microsoft's data science team, "we spent time understanding the environment, required skill sets, and began ingesting various data components within Azure ML to provide predicted outcomes," says Brian Balzer, vice president of digital technology and business transformation at G&J Pepsi.
In the computing world we have gone through several phases. In the beginning organisations usually had one large mainframe computer. This was followed by the era of dumb terminals and eventually by the era of personal computers – the first-time end-users owned the hardware that did the processing. Several years later cloud computing was introduced. People still owned personal computers, but now accessed centralised services in the cloud such as Gmail, Google Drive, Dropbox, Trello, and Microsoft Office 365.
The stock market trembled in the midweek after the Federal Reserve announced Wednesday, 16 June, that it plans to hike interest rates as early as 2023. And though the Nasdaq NDAQ and S&P 500 both recovered fairly quickly from Wednesday's midafternoon slump, the Dow Jones Industrial Index wasn't quite so fortunate. The Fed's policy update deviates from previous estimates from the Fed, which pushed rate hikes out into 2024 and beyond. And while there was no mention of when the Fed will start to roll back its $120 billion per month bond purchase program, rest assured, investors are poised for that blow, too. But until that happens, we can all sit back, breathe a sigh of relief that the Fed didn't sweep the rug out from under our investment accounts overnight, and enjoy this week's trending stocks courtesy of Q.ai. Q.ai runs daily factor models to get the most up-to-date reading on stocks and ETFs.
This Week Top Automation write-ups highlight the concern of experts over the use of machine learning tools to create deepfake which could be used to fulfill illegal interests on a larger scale. At one level, the technology is threatening us but on the other hand, it will be helping to provide AI-based ID verification for transactions and other purposes. Moreover, Google just announced that their web services are undergoing a significant transformation and workspace will be available for all users. While Microsoft will allow their Xbox One owners to play next-gen Xbox games through the xCloud service. There is much more to Explore.
Technology has been evolving for decades from the 1950s till 2021, things have changed drastically. The current trend of Artificial Intelligence takes over the present stock market. It has been attracting numerous companies to adapt to the trend, driving investments towards them, due to its increasing demand in the present and future. Investing in digital technologies can create huge revenue in the coming days. Ark Invest, an investment management firm, estimates that Artificial Intelligence is going to add US$30 trillion to the global economy by 2037.
Microsoft is using its machine learning technology Azure to fight climate changes, pollution, and other environmental complexities. Azure is providing AI-based computing solutions to work on environmental sustainability projects. Our planet is currently facing a climate crisis and several large tech companies have come forward to assist scientists and researchers to improve the deteriorating situation. Microsoft has enabled its AI and machine learning technologies to fight against such anomalies and drive our planet towards a sustainable future. The company has developed two APIs especially made for Earth and continues to work on more such technologies and initiatives.