Police and border guards must combat racial profiling and ensure that their use of "big data" collected via artificial intelligence does not reinforce biases against minorities, United Nations experts said on Thursday. Companies that sell algorithmic profiling systems to public entities and private companies, often used in screening job applicants, must be regulated to prevent misuse of personal data that perpetuates prejudices, they said. "It's a rapidly developing technological means used by law enforcement to determine, using big data, who is likely to do what. And that's the danger of it," Verene Shepherd, a member of the UN Committee on the Elimination of Racial Discrimination, told Reuters. "We've heard about companies using these algorithmic methods to discriminate on the basis of skin colour," she added, speaking from Jamaica.
AI-enabled software development tools enable businesses to perform tasks with efficiency and accuracy. Software application contributes significantly to the routine activities across organizations. From searching a product over the internet to sending emails to clients and colleagues, the utilization of software in business has accelerated. Though the software is a compelling entity, its development is a tough task. It is a complex process that requires ideation, product definition, strategic designing, coding, quality assessment, and coding. Additionally, if any step in the software development goes wrong, the entire process needs to be started again.
In Autumn 2019, as CIOs carefully crafted their technology investment plans for the new year, artificial intelligence and other advanced analytics made up a key part of the initiatives earmarked for investment. It was during that October to December time period when Deloitte was surveying IT leaders about the state of AI in their enterprises for a report released in July 2020. But as we all know, the tech plans those leaders set late last year hit a surprise challenge in the form of the COVID-19 pandemic that shut down economies and changed the way we work. But instead of throwing out those plans to preserve investment for other priorities, AI and advanced analytics became one of the top priorities, according to Beena Ammanath, executive director of the Deloitte AI Institute. Rather than cut back on these initiatives to preserve cash during what started as an unpredictable crisis, IT leaders instead accelerated their investments in these technologies.
Disruptive technologies like advanced analytics, advanced robotics, big data, learning machines, the internet of things, 3D printing, and wearables are finding their way into production lines. Notwithstanding the sluggishness of progress on today's plant floors, the digital wave is gradually changing assembling, adding to significant productivity improvements and the rise of innovative production paradigms that deliver more customized and proficient solutions. In the interim, automation is the innovation that empowers machines to play out specific operations. This chops down the amount of human work required. "A few jobs are unhygienic and dangerous, and they are not appropriate for laborers," clarifies Crystal Fok, an Associate Director of MPE Cluster and Robotics Platform.
But as your data scientists and data engineers quickly realize, building a production AI system is a lot easier said than done, and there are many steps to master before you get that ML magic. At a high level, the anatomy of AI is fairly simple. You start with some data, train a machine learning model upon it, and then position the model to infer on real-world data. Unfortunately, as the old saying goes, the devil is in the details. And in the case of AI, there are a lot of small details you have to get right before you can claim victory.
AutoML is poised to turn developers into data scientists -- and vice versa. Here's how AutoML will radically change data science for the better. In the coming decade, the data scientist role as we know it will look very different than it does today. But don't worry, no one is predicting lost jobs, just changed jobs. Data scientists will be fine -- according to the Bureau of Labor Statistics, the role is still projected to grow at a higher than average clip through 2029.
Search is one of the oldest technologies around. Ever since the dawn of the World Wide Web, a search engine has been the portal through which we obtain information. The search for a better search engine index kick started the Hadoop craze, and it continues to drive Google to push the limits of technology. But don't for a second think that search has been solved. "Search is far from being solved. It's the hardest thing we do. It's the hardest thing everybody does."
Data science is one of the most appealing industries with a lot of features and opportunities. With humans using 2.5 quintillion data per day, the data landscape is at a dynamic space, almost mimicking the real global connectivity. New technologies to tackle data overwhelming are introduced year after year and the transformation is likely to continue into the coming decade. The rise for data-related practitioners in the fast-moving world is very real. According to a report, data related jobs post 2020 is anticipated to add around 1.5 lakh new openings.
As companies continue to navigate the challenges brought on by the COVID-19 pandemic, we have seen a range of market developments and business adaptations that impact the IT organization's future. We have seen a new push to consolidate the IT labor portfolio to drive cost synergies. Companies are looking to quickly adopt AI and automation tools to accelerate the speed of delivery and enable innovation. Clearly, IT organizations must move quickly to succeed in this new paradigm. Here are a few strategies to address these developments and pivot from survival to success, where IT organizations must take immediate, aggressive steps to adapt to the faster business and IT environment.We recommend short-term prescriptive actions related to the IT organization across these dimensions: Baselining current operations can help determine how to redeploy underutilized resources or restructure teams to support strained areas and newly reprioritized strategic initiatives.