In an era where every click, tap or keystroke leaves a digital trail, Americans remain uneasy and uncertain about their personal data and feel they have little control over how it’s used. This wariness is even ticking up in some areas like government data collection, according to a new Pew Research Center survey of U.S. adults conducted May 15-21, 2023. Today, as in the past, most Americans are concerned about how companies and the government use their information. But there have been some changes in recent years: Read more...
How billion-dollar fines are reshaping digital communications in banking
In a workplace that’s become increasingly dominated by instant messaging apps like Slack, global banks (and regulators) are taking unprecedented steps to police how their employees communicate. HSBC is blocking staff from texting on their work phones, a person familiar with the matter confirmed to CNN. The ban was first reported by Bloomberg. The move comes after 11 brokerage and investment firms were fined $549 million this August by the Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC) over their use of messaging apps like WhatsApp. What’s happening: Banks must follow strict compliance rules for how they use and store their employees’ texts and other business communications. But WhatsApp and other instant messaging apps can be particularly problematic because they’re often connected to bankers’ personal devices and are difficult for compliance departments to monitor for recordkeeping. Financial institutions fear that there may be more more missteps and fines to come, so they’re proactively limiting how their workers communicate about official business. Read more
Machine Learning & Programming
Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines "discover" their "own" algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches. Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine, where it is too costly to develop algorithms to perform the needed tasks. The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning. ML is known in its application across business problems under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods. Read more...
Role of Artificial intelligence in healthcare
Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.
The primary aim of health-related AI applications is to analyze relationships between clinical data and patient outcomes. AI programs are applied to practices such as diagnostics, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. What differentiates AI technology from traditional technologies in healthcare is the ability to gather larger and more diverse data, process it, and produce a well-defined output to the end-user. AI does this through machine learning algorithms and deep learning. Because radiographs are the most common imaging tests conducted in most radiology departments, the potential for AI to help with triage and interpretation of traditional radiographs (X-ray pictures) is particularly noteworthy. These processes can recognize patterns in behavior and create their own logic. To gain useful insights and predictions, machine learning models must be trained using extensive amounts of input data. AI algorithms behave differently from humans in two ways: (1) algorithms are literal: once a goal is set, the algorithm learns exclusively from the input data and can only understand what it has been programmed to do, (2) and some deep learning algorithms are black boxes; algorithms can predict with extreme precision, but offer little to no comprehensible explanation to the logic behind its decisions aside from the data and type of algorithm used.
As widespread use of AI in healthcare is relatively new, research is ongoing into its application in various fields of medicine and industry. Additionally, greater consideration is being given to the unprecedented ethical concerns related to its practice such as data privacy, automation of jobs, and representation biases. Furthermore, new technologies brought about by AI in healthcare are often resisted by healthcare leaders, leading to slow and erratic adoption. Read more...
Pay Transparency Is Sweeping Across the US
Applying for a new job is always a venture into the unknown, but when it comes to the pay on offer, that uncertainty is lessening. Salary disclosure in US job ads appears to now be the norm. New data from job marketplace Indeed shows that as of August more than half of US job postings on the site included a salary range. Pay transparency laws have recently spread across the US, taking effect in Colorado in 2021, New York City in 2022, and California and Washington states this year. New York state enacted its own law yesterday. But the trend to more openness about pay may also reflect a growing awareness that pay transparency is good for business. Indeed surveyed US job seekers earlier this year and found that 75 percent of them would be more likely to apply for a job if it included salary data. Postings that included pay rates attracted 30 percent more applicants on the site. “With the tight labor market, pay transparency seems to be one of the new tactics employers can use to attract workers,” says Corey Stahle, an economist at Indeed who conducted the study. Read more...
X, formerly known as Twitter, will collect user biometric data, job and education history
X, the platform formerly known as Twitter, will begin collecting biometric data and information on users’ employment and education history starting next month, according to the site’s updated privacy policy. “Based on your consent, we may collect and use your biometric information for safety, security, and identification purposes,” X said in the new privacy policy, set to go into effect on Sept. 29. The social media platform told Bloomberg Law that the biometric data collection is for X Premium users, or those who pay for the platform’s subscription service, to allow for an additional layer of verification. X did not specify what biometric data it plans to collect. However, such data can include facial images, fingerprints and iris patterns. The new addition to X’s privacy policy comes as the company faces a proposed class action lawsuit in Illinois over allegations that it collected biometric information on users without providing advance notice or obtaining their consent. Read more...
The American dream is no longer about getting rich
The American dream is no longer about getting rich, owning a home for small business owners, survey finds — here’s what the cost-of-living crisis has these folks chasing instead
Over half of entrepreneurs surveyed (56%) think they’ll have achieved the American Dream when they’re living comfortably — which requires a certain level of financial security and wealth. There are many ways to build wealth. For instance, you can stash away some of your monthly income in a high-yield savings account, a certificate of deposit (CD), or a tax-advantaged retirement account like a 401(k) or an IRA. You can also invest any spare cash to help generate passive income through dividends. Of course, it takes some time to reap the benefits of savings and investments — which could be why Gen X and boomers are more focused on living comfortably (after many years of building wealth) than millennials and Gen Z. But even the cost of living comfortably has increased along with the price of almost everything else. On average, you would need to be earning $68,499 a year after taxes to live comfortably, according to a recent Smart Asset study. That’s 20% higher than the average amount of $57,013 needed in 2022. Read more...
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