Today Juniper Networks announced it was acquiring smart wide area networking startup 128 Technology for $450 million. This marks the second AI-fueled networking company Juniper has acquired in the last year and a half after purchasing Mist Systems in March 2019 for $405 million. With 128 Technology, the company gets more AI SD-WAN technology. SD-WAN is short for software-defined wide area networks, which means networks that cover a wide geographical area such as satellite offices, rather than a network in a defined space. Today, instead of having simply software-defined networking, the newer systems use artificial intelligence to help automate session and policy details as needed, rather than dealing with static policies, which might not fit every situation perfectly.
Always worried about the potential for embarrassing background noises at home during video meetings? Microsoft is working on an update that could save you from future videoconferencing faux pas. The company's Microsoft 365 roadmap lists as in development "AI-based real-time noise suppression," which is scheduled for release in November 2020. The feature, spotted by news site Windows Latest, "will automatically remove unwelcome background noise during your meetings." Artificial intelligence technology is used to analyze a user's audio and "specially trained deep neural networks" will filter out noises and keep the person's voice, the software giant's planning document says.
In the age of big data, the challenge is no longer accessing enough data; the challenge is figuring out the right data to use. In a past article, I focused on the value of alternative data, which is a vital business asset. Even with the benefits of alternative data, however, the wrong data granularity can undermine the ROI of data-driven management. "We're so obsessed with data, we forget how to interpret it". So how closely should you be looking at your data?
Rapid advances in technology, connectivity and telecommunications are conspiring to make Africa's large, rapidly growing population a valuable asset for the automation revolution. It is imperative that Africa quickly develop agency in data and artificial intelligence and it will be lucrative for investors who support them by financing Africa's telecom and data backbone. Africa must urgently develop cogent digital strategy. This at first seems fanciful, or even superfluous, given the continent's relative lack of more basic development. Indeed, there are myriad other challenges to which most would assign primacy.
Google Data Studio (GDS) is a free dashboard and reporting tool (which lives in the cloud). It allows you to create dynamic, collaborative reports, and visualization dashboards. Paid Business Intelligence and Data Analytics Tools Like Tableau Are have either plateaued or will plateau soon. Many of these are either too expensive for small or teams or have a steep learning curve for beginners. This course helps you start with GDS and become proficient in producing powerful visualizations and reports.4.5/5
The growing demand for professionals in the areas of Artificial Intelligence and Data Science has reflected in the new-generation interdisciplinary programmes proposed by government and aided colleges under Mahatma Gandhi University (MGU) for the academic year 2020-21. The university had invited applications from higher educational institutions based on the government directive that such courses could be launched from November 1. The Higher Education Department had asked universities to initiate steps to launch new undergraduate and postgraduate programmes in innovative areas. They included four- and five-year programmes recommended by an expert committee set up by the government. The integrated M.Sc programme in Computer Science (Artificial Intelligence and Machine Learning) figured top among the innovative programmes proposed by the affiliated colleges.
Welcome! It's been a while since I last posted on the platform. However, this time, I shall solely focus on the specialized field of Data Science rather than my ideological views. The field has grown over the years coupled with great innovations. As a learner/contributor/beginner in the Data Science field, do you really need to learn to code to build a model? In this post, we shall review two different steps to build a predictive model with the use of the Jupyter Notebook lab and Microsoft Azure platform.
Confucius once said, "Fish forget they live in water; people forget they live in the Tao" (Lin, 2007). Analogously, it may be easy for data scientists to forget they live in a world defined and permeated by mathematics. The two pieces, "Ten Research Challenge Areas in Data Science" by Jeannette M. Wing and "Challenges and Opportunities in Statistics and Data Science: Ten Research Areas" by Xuming He and Xihong Lin, provide an impressively complete list of data science challenges from luminaries in the field of data science. They have done an extraordinary job, so this response offers a complementary viewpoint from a mathematical perspective and evangelizes advanced mathematics as a key tool for meeting the challenges they have laid out. Notably, we pick up the themes of scientific understanding of machine learning and deep learning, computational considerations such as cloud computing and scalability, balancing computational and statistical considerations, and inference with limited data.
Using AI to manage COVID-19 risks and applying predictive models for multiple kinds of retail. U.K. retailers are applying AI to track customer feedback and manage new risks caused by the COVID-19 pandemic. Nike is using predictive models to optimize warehouse inventory. A U.K. retail group is increasing its investment in AI and predictive analytics after a trial run reports great results.
In recent times, there has been an exponential growth of data science and machine learning applications. With the advent of these data-driven applications, python is the go-to choice for most of the developers. In the last few years, python has jumped to the number one position for the languages used for ML which is mostly due to the convenience of packaging the models and exposing them as a service. Model preparation and training is one of many steps in the machine learning lifecycle. Besides an active ML application, there go multiple things that work concurrently under the hood. On a high level, it includes Data cleaning and preparation, selecting and fine-tuning algorithms, model training, delivering the model prediction in the form of an endpoint by exposing an endpoint know as "API".