Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
With the ever-increasing volume, variety, and velocity of available data, scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms enabling us to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. A question that frequently comes up in many data-related discussions is what the difference between DS, ML, and AI is? Can they even be compared? Depending on who you talk to, how many years of experience they have had, and what projects they have worked on, you may get widely different answers to the above question. In this blog, I will attempt to answer this based on my research, academic, and industry experience; and having facilitated numerous conversations on the topic.
Smart & Final is rolling out Hypersonix's AI-driven analytics platform to support the company's enterprise analytics and digital transformation initiatives. The two companies started working together sixty days ago on a successful pilot program. With this announcement, Smart & Final officially joins a handful of early adopters in the grocery and consumer-commerce industries turning to the innovative company to help navigate the post-COVID-19 market. "Hypersonix is a key ingredient in leveraging actionable analytics that can be operationalized by our business teams as part of our on-going digital transformation," said Ed Wong, EVP and Chief Digital Officer at Smart & Final. "We established a great innovation-centric collaboration with Hypersonix where we are finding new ways to address our needs in key strategic areas for our business."
On Tuesday, a number of AI researchers, ethicists, data scientists, and social scientists released a blog post arguing that academic researchers should stop pursuing research that endeavors to predict the likelihood that an individual will commit a criminal act, as based upon variables like crime statistics and facial scans. The blog post was authored by the Coalition for Critical Technology, who argued that the utilization of such algorithms perpetuates a cycle of prejudice against minorities. Many studies of the efficacy of face recognition and predictive policing algorithms find that the algorithms tend to judge minorities more harshly, which the authors of the blog post argue is due to the inequities in the criminal justice system. The justice system produces biased data, and therefore the algorithms trained on this data propagate those biases, the Coalition for Critical Technology argues. The coalition argues that the very notion of "criminality" is often based on race, and therefore research done on these technologies assumes the neutrality of the algorithms when in truth no such neutrality exists.
Few issues are as important to businesses today than sustainability. Because the modern consumer cares about the environment, companies need to meet higher expectations about eco-friendly practices. Supply chains, in particular, have a lot of room to improve. It's no secret that logistics chains aren't exactly eco-friendly. They account for more than 80% of carbon emissions globally. The modern business world can't exist without supply chains, but the natural world won't exist in the same way if they don't improve. The good news is there's an . . .
The world is in the midst of a historical turning point. The COVID-19 pandemic has effectively halted life as we once knew it, and left the open question, "what will our world look like when'normal' life resumes?" While we don't have a crystal ball that allows us to peer into the future, history has given us a template on what to expect. Past pandemics have shaped politics, crashed economies, purred revolutions and produced other profound societal transformations. In the 14th century, the bubonic plague killed more than 60 percent of Europe's population – a dramatic population decline that actually improved living standards for the survivors and marked the decline in serfdom.
Artificial intelligence is beginning to be usefully deployed in almost every industry from customer call centers and finance to drug research. Yet the field is also plagued by relentless hype, opaque jargon and esoteric technology making it difficult for outsiders identify the most interesting companies. To cut through the spin, Forbes partnered with venture firms Sequoia Capital and Meritech Capital to create our second annual AI 50, a list of private, U.S.-based companies that are using artificial intelligence in meaningful business-oriented ways. To be included, companies had to be privately-held and focused on techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language), or computer vision (which relates to how machines "see"). The list was compiled through a submission process open to any AI company in the U.S. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency).
An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital--before they to return to the doctor's office with further complications. But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that aren't optimized for their patients. Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.
BPU Holdings is a global company, headquartered in Korea that pioneers in the development of Artificial Emotional Intelligence (AEI). The mission of the company is to generate the most advanced, secure usable, and innovative Artificial Emotional Intelligence technology in the world. BPU has developed the first Artificial Emotional Intelligent (AEI) platform -- AEI Framework, which emulates how people think and feel. BPU improves the human condition by offering rigorous tools to improve emotional intelligence. Tracking and handling emotions enable the management of professional and interpersonal relationships, empathetically and judiciously.
Artificial intelligence (AI) and machine learning (ML) have the power to deliver business value and impact across a wide range of use cases, which has led to their rapidly increasing deployment across verticals. For example, the financial services industry is investing significantly in leveraging machine learning to monetize data assets, improve customer experience and enhance operational efficiencies. According to the World Economic Forum's 2020 "Global AI in Financial Services Survey," AI and ML are expected to "reach ubiquitous importance within two years." However, as the rise and adoption of AI/ML parallels that of global privacy demand and regulation, businesses must be mindful of the security and privacy considerations associated with leveraging machine learning. The implications of these regulations affect the collaborative use of AI/ML not only between entities but also internally, as they limit an organization's ability to use and share data between business segments and jurisdictions.