One of the byproducts of our digitally transformed world is the accumulation of large quantities of data. Online transactions, medical records, social media posts, emails, instant messages, and connected sensors are just a few examples of the kinds of data being captured and stored on a daily basis. Scientists and research organizations have been exploring how to leverage big data for artificially intelligent applications since the 1970s. Nonetheless, until fairly recently, the big data issues for enterprises remained how to store it cost effectively, how to retrieve it efficiently when needed, and how to protect it from unauthorized access. The growth of the cloud opened up a whole new realm of cost-effective data storage and retrieval solutions, but big data was still largely perceived by enterprises as a passive asset that did not contribute significantly to their bottom lines.
Artificial intelligence startup Data Skrive doesn't want to take jobs away from sports reporters--just free them from the statistical grind. What if I told you that most of the sports stories you'll read in the future will be written by robots? Well, actually, you probably already have perused a few AI-generated articles on athletics and didn't even realize it. The Hawaii baseball team pounded out 15 hits en route to a 13-6 rout of UC Davis today at Les Murakami Stadium. By winning two of this three-game set, the Rainbow Warriors won a series for the second time this season.
The AI Times is a weekly newsletter covering the biggest AI, machine learning, big data, and automation news from around the globe. If you want to read A I before anyone else, make sure to subscribe using the form at the bottom of this page. The R&D Labs initiative will be part of the Fintech Station, an accelerator launched by Finance Montreal, and will also be part of the venture community hub, Espace CDPQ. For Sean, success revolves around ingraining these three facets into a company's culture. A team that can operate with focus, speed, and intensity is unstoppable – and it's one of the unique advantages startups have overall in the market.
Not long after the invention of computers in the 1940s, expectations were high. Many believed that computers would soon achieve or surpass human-level intelligence. Herbert Simon, a pioneer of artificial intelligence (AI), famously predicted in 1965 that "machines will be capable, within twenty years, of doing any work a man can do"--to achieve general AI. Of course, these predictions turned out to be wildly off the mark. In the tech world today, optimism is high again.
From personalizing customer experience to automating processes, Deep Learning applications are offering smart solutions to businesses across industries, opening up a world of opportunities for them. Deep Learning algorithms use sophisticated structures, such as Convolutional Neural Networks, belief networks, or recurrent neural networks. Effective DL frameworks also help simplify the implementation of large and complex models like Convolutional Neural Networks. In this post, we present the top Deep Learning frameworks preferred by data scientists and Deep Learning experts across the globe. We have also included the major pros and cons of each framework, enabling you to choose the right one for your upcoming project.
In today's digital world, when more and more companies rely on e-commerce to reach a larger international audience, the logistic industry is now more important than ever for the business landscape. However, with the world's logistical requirements becoming more complex and the rising amount of data, the logistics industry is also facing more challenges than ever before. The logistics industry relies on a multitude of processes in order to facilitate the proper distribution of materials, products, and services from businesses to customers or from businesses to other businesses. Thus, with the rapid advancements of our world and the increasing need for logistical services, the industry is looking towards a future of digitally-optimized processes. Here's how the AI and automation are revolutionizing the logistics industry and contribute to the improvement of it: Customers service in the logistics industry is a vital part of the success of a logistics provider.
The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving their goals. Thus, a certain level of freedom to choose the best path to a specific goal is necessary in making AI robust and flexible enough to be deployed successfully in real-life scenarios. This is especially true when AI systems tackle difficult problems whose solution cannot be accurately defined by a traditional rule-based approach but require the data-driven and/or learning approaches increasingly being used in AI. Indeed, data-driven AI systems, such as those using machine learning, are very successful in terms of accuracy and flexibility, and they can be very "creative" in solving a problem, finding solutions that could positively surprise humans and teach them innovative ways to resolve a challenge. However, creativity and freedom without boundaries can sometimes lead to undesired actions: the AI system could achieve its goal in ways that are not considered acceptable according to values and norms of the impacted community.
Data analytics in marketing have long been among the more sophisticated approaches found within companies. All sorts of predictive and prescriptive analytics are used to segment customers, identify which of them are most likely to buy, and which prices, promotions, and incentives will push them into converting. It's just as obvious that many firms have moved quickly toward digital marketing as their main growth driver for client acquisition and retention. Customers are spending vast amounts of time and attention across digital channels, and marketers are moving wherever they go with increasing spend online. A single company--even a relatively small one--may employ search engine optimization, online advertising, social media marketing, video advertising, landing pages, emails, and even more types if you include offline channels.
That, in the opinion of Derek Manky, Global Security Strategist for Fortinet, is what is coming down the line at us all as the next major security threat. The combination will come in the form of Hivenets and Swarmbots, and the results could be far more targeted and focused attacks, based not on the basic process of breaking into a system with one malware exploit and launching an attack. Instead it will be based on inserting untold numbers of Bots into systems to observe the activities and identify the weak-points and collectively decide where and when to attack. Manky outlined this attack model at the recent seminar Fortinet held at its Sophia Antipolis facility just outside Nice, where he discussed some of the cyber security threats he sees coming though over the next 12 months or so. The idea behind the new developments will be no surprise to those of an entomological persuasion, for the idea is to get as many small Bots onto systems and communicating in the same way that insects do when they are in a hive or when swarming.