What’s the difference between that and machine learning? or does that mean you work on artificial intelligence? The fields do have a great deal of overlap, and there’s enough hype around each of them that the choice can feel like a matter of marketing. But they’re not interchangeable: most professionals in these fields have an intuitive understanding of how particular work could be classified as data science, machine learning, or artificial intelligence, even if it’s difficult to put into words.
An aspiring data scientist needs to build useful stuff, learn new things, demonstrate that he can deliver value using data analytics and work with others using the same tools. Tips and tricks can sometimes get us ahead by a bit, but the fundamentals matter the most.
How much AI needs to mimic the brain has been debated for decades. The recent success of deep learning, which is only loosely related to brains, has bolstered the argument that AI can advance without brain theory. But that success has also brought to the fore the limits of deep learning, making it more obvious that new approaches are needed.
Most IoT deployments are cross-functional. They involve the use of both hardware and software. And IoT-enabled solutions offer several competitive advantages from a supply chain security perspective. Learn what you need to consider building a winning strategy.
Data Scientists at Work displays how some of the world’s top data scientists work across a dizzyingly wide variety of industries and applications — each leveraging her own blend of domain expertise, statistics, and computer science to create tremendous value and impact.
We’d be better served by focusing on human’s irrational thought patterns than fearing some AI-enabled bogeyman. Doing so would shed light on our illogical biases and thought patterns that can intrude into AI algorithms. It would also help understand how AI can overcome our inherent intellectual handicaps and boost productivity
Today we are in the 4th Industrial Revolution and no doubt that this era would be driven by advancement in Artificial Intelligence and Robotics. This is the era where man and the machine are going to co-exist, collaborate and work together. So the union of man and machine is inevitable and it is up to us as to how we would exploit this situation to our advantage and live happily ever after!
Data science involves hypothesizing or discovering systematically observable properties of a phenomenon. It can be used to discover correlations (What phenomena occurred) but cannot be used to establish causality (Why the phenomena occurred).
Throughout the past couple of years, we’ve seen a growing rollout of robotic and software-process automation systems. However, adoption rates have been fairly slow, which sped up quite a bit over the past year. It also means 2018 will be hugely impacted, maybe even disrupted, by even stronger growth and adoption rates of automation.
We all need to focus on expanding our understanding of the automation revolution, and based on that understanding, identify and develop the skills that will be necessary to survive and succeed in this new world.
The year 2018 will be another landmark year in the database space as innovation in cloud and AI continues to drive customer engagement. Here are five predictions for the year ahead that will impact data at a foundational level and influence the customer experience.
IoT is expected to continue its explosive adoption trend, and it is important to continue to be mindful of the basic tenets of how to build and deploy connected devices in ways that deliver robust considerations of both security and privacy.
Many patients who experience adverse events, such as respiratory depression, do not follow any simple criteria for determining whether they will be at risk for obstructive or central sleep apnea. Combining analysis with real-time data at the point of collection creates a powerful tool for prediction and clinical decision support.
Understanding the implications of location data quality and the associated location intelligence is necessary for correct business decisions. Availability of location signals can guide audience building, audience behavior monitoring, provide insights about consumer activity in the physical world, and track, measure post-campaign activity in the physical world to name a few key use-cases. Furthermore, combining location signals with other data sources can guide broader business decisions.
Financial disruption doesn’t happen fast. There aren’t any outright winners. It would be great if there was a financial Uber or Airbnb that could flip the industry overnight, but that’s just not the nature of the space. Rather than criticizing the industry’s shortcomings or writing it off as a failure, we need to do a better job of recognizing just how much fintech has accomplished over the last few years.
Enterprise AI is the new hot topic in technology, especially as the consumer space blossoms with sales and adoption. Consumers push the expectations of AI for the business to new heights – and if not carefully prepared, solutions will inevitably fail.
Data science jobs are among the most challenging to fill, taking five days longer to find qualified candidates than the market average. Employers are willing to pay premium salaries for professionals with expertise in these areas as well. The most in-demand jobs in data science require advanced education, further driving up demand and salaries for professionals with these qualifications.
Machines are fast, impartial and relentless. This helps them do amazing things. But this side of the robot apocalypse, the real challenge of AI is how it forces us to reconceive our humanity. We have long viewed ourselves as the smartest beings on earth. Intelligent machines may knock us off this pedestal.
Has the hype around AI gone one step too far? Are we really on the brink of a rise-of-the-machines takeover, or should we view intelligent technology as a means to improve our marketing efforts and build a better relationship with consumers?
As part of intelligent automation investigations, robotic process automation (RPA) is being explored in many sectors across the globe, to increase the efficiency of operations and create new products and services. Are you exploring automation and AI for your organization?
A relation with a customer is not over after the delivery of a product or a service, in fact a relation with a customer goes way beyond making a sale. This is why businesses need customer relationship management, to help out a customer facing a problem with your service/product.
Are AI-based systems the next step in a continuous tech evolution that’s allowing us to achieve more, or something alarming that society should be concerned about? Before leaning on either side of the coin, it is important to first understand the misconceptions of AI as portrayed in popular media.
With more and more money pouring into internet companies, companies have been collectively spending billions of dollars in growing the user base on their platform and fight off the cutthroat competition to survive in their respective domain or vertical. Not many companies can survive at scale if the Churn problem is not mitigated as soon as possible.
Are you a tech geek? A business owner? Do you know how technology affects marketing? Are you constantly looking for solutions to make your processes easier? Here are some of the most interesting current and upcoming future trends in technology.