The "Autonomous Mobile Robots Market Research Report: By Offering, End User – Global Industry Size, Share and Trends Analysis, Forecast to 2030" report has been added to ResearchAndMarkets.com's offering. The artist with the remote-controlled robotic body: 'I've made a career out of being a failure'
Micro Focus (NYSE: MFGP) today announced the Vertica 10 Analytics Platform, which includes major updates for operationalizing machine learning at scale and expanding deployment options for Vertica in Eon Mode, enabling the most intensive variable workloads across major cloud and on-premises data centers. With Vertica 10, organizations are better equipped to unify their data siloes and take advantage of the deployment models that make sense now and in the future in order to monetize exponential data growth and capture real-time business opportunities. "Over the years, many organizations have successfully captured massive amounts of data, but are now challenged with getting the business insights they need to become data-driven. The market demand to leverage cloud architectures separating compute from storage needs to be balanced with the higher costs and increased risk of cloud-only data warehouses, while machine learning projects with tremendous potential have struggled to make their way into production," said Colin Mahony, Senior Vice President and General Manager, Vertica, Micro Focus. "Vertica 10 expands the options for a unified analytics strategy to address growing data siloes, a mix of cloud, on-premises, and hybrid environments, and the pressing need to operationalize machine learning at scale."
In an increasingly competitive world, we should have a deep understanding of the business in which we operate, how it is evolving, and the new innovations that we could embrace or build to remain competitive and conquer new market segments. To do this, we must be able to develop a clear vision of transformation that takes us to another level of performance. By embracing Digital Transformation, we will deal with artificial intelligence, machine and deep learning, virtual reality, and a lot of other innovative technologies. At first sight, it might even sound fearful to lead the business in such a complex and intricate direction. With this in mind, we will consider some strategies to better understand and take competitive advantage of the huge streaming of data in the current era of the digital revolution.
If you've ever been curious to learn about AI and computer vision, check out the great podcast Reid Jackson put together with our friends from GS1 US on LexSet, interviewing me and Francis Bitonti. Where I sit down with the founders of #startup LexSet to discuss #computervision modeling, bias & more http://ow.ly/sQer30quzmy
Artificial intelligence (AI) is slowly becoming more mainstream, as companies amass large amounts of data and look for the right technologies to analyze and leverage it. That's why Gartner predicted that 80% of emerging technologies will have AI foundations by 2021. With the trend towards predictive analytics, machine learning and other data sciences already underway, marketers need to start paying attention to how they can leverage these techniques to form a more data-driven marketing strategy. With this in mind, we've asked AI industry experts why marketing leaders need to start considering AI, and some of the best open-source AI frameworks to keep tabs on. Dean Abbott, chief data scientist and co-founder of SmarterHQ, believes AI should be top of mind for most business leaders.
If you do some Google searches for the term "how to invest in artificial intelligence stocks," you'll find a plethora of opinions about what companies you ought to be looking at. Typically, you'll get the "invest in everything with Google" type recommendations which just list some popular tech stocks along with the obligatory NVIDIA (NVDA) mention. Aside from investing in AI chips with NVIDIA, pure-play AI stocks have been far and few between. Now that most companies use machine learning in some way, the definition of an "AI stock" is becoming ever more blurry. As with any disruptive technology, machine learning is changing quickly.
ERP systems need to lose their cumbersome heritage and open up to third-party applications, in order to help businesses benefit from technological innovations more quickly. Artificial intelligence (AI) will have a significant impact on companies and their business models over the next five years--85 percent of CEOs surveyed in PwC's 22nd Annual Global CEO Survey are convinced of this. But with only 33 percent having dipped their toe into AI for'limited uses', and fewer than one in ten using it on a wide scale, the range of applications has been limited so far. However, this is soon set to change. Despite the use of AI being a distant dream for many businesses, the current maturity of intelligent technologies and the expectations of enterprise resource planning (ERP) systems in particular--to support innovations--have fundamentally changed business demands.
Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data In this video, I will be showing you how to perform principal component analysis (PCA) in Python using the scikit-learn package. PCA represents a powerful learning approach that enables the analysis of high-dimensional data as well as reveal the contribution of descriptors in governing the distribution of data clusters. Particularly, we will be creating PCA scree plot, scores plot and loadings plot. This video is part of the [Python Data Science Project] series. If you're new here, it would mean the world to me if you would consider subscribing to this channel.
Google Brain had recently launched the TensorFlow Developer Certificate program which would enable machine learning (ML) enthusiasts to demonstrate their skills in using TensorFlow to solve deep learning and ML problems. According to the blog post, the goal of this certificate is to provide them with the opportunity to showcase their expertise in ML in an increasingly AI-driven job market. TensorFlow is one of the popular open-source libraries in ML which provides a suitable abode with essential tools for ML researchers and developers to perform SOTA ML applications. The developers at Google Brain claim that this is intended as a foundational certificate for students, developers, and data scientists who want to demonstrate practical ML skills through building and training of models using TensorFlow. Currently, this is a level one certificate exam which tests a developer's foundational knowledge of integrating ML into tools and applications.
Machine learning (ML) practitioners gather data, design algorithms, run experiments, and evaluate the results. After you create an ML model, you face another problem: serving predictions at scale cost-effectively. Serverless technology empowers you to serve your model predictions without worrying about how to manage the underlying infrastructure. Services like AWS Lambda only charge for the amount of time that you run your code, which allows for significant cost savings. Depending on latency and memory requirements, AWS Lambda can be an excellent choice for easily deploying ML models.