If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Salesforce and IBM announced an expansion of their strategic partnership on Friday, with the firms combining the power of IBM Cloud and Watson services with Salesforce Quip and Salesforce Service Cloud Einstein, the firms announced in a joint press release Friday. Two top tech firms like Salesforce and IBM connecting their artificial intelligence (AI) platforms reinforces the growing value of AI and big data in the enterprise. AI, especially, is taking center stage as one of the battleground technologies for business, and this is a clear example of two CEOs making a move to reinforce that with their partnership. In the release, IBM CEO Ginni Rometty said that the combination of Watson and Einstein will "help enterprises make smarter business decisions." Salesforce CEO Marc Benioff echoed this sentiment, saying in the release that the combo will "deliver even more innovation to empower companies to connect with their customers in a whole new way, leveraging the power of the cloud and AI." SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) Specifically, the Watson/Einstein combination will provide actionable next steps in a given process, the release said.
Google on Wednesday released its Cloud AutoML Vision service in Alpha. It is the first in a planned series of Cloud AutoML services designed to help people with limited machine learning expertise build their own custom models using advanced techniques such as learning2learn and transfer learning. Learning2learn is a process for automating machine learning, while transfer learning "takes a fully trained model for a set of categories and retrains it from the existing weights for new classes," a Google Cloud spokesperson told the E-Commerce Times in a statement provided by company rep Danny McCrone. Cloud AutoML Vision makes it faster and easier to create custom ML models for image recognition. Its drag-and-drop interface lets users upload images, train and manage models, then deploy those trained models directly on Google Cloud.
IBM and Salesforce announced Friday an expansion of their strategic partnership that brings more data integration to companies so they can better interact with customers. SaaS had a major impact on the way companies consume cloud services. This ebook looks at how the as a service trend is spreading and transforming IT jobs. Under the extended partnership, IBM will build a Watson app for Salesforce's Quip Live Apps, launching AI tools on the collaborative document platform. Salesforce introduced Live Apps in November 2017 to be embedded directly into any Quip document.
This article was written by Hardik Gohil, Sr Content Writer. Artificial Intelligence has effectively convinced its necessity to the entire world by performing excellently in various industries. Almost all the industries including manufacturing, healthcare, construction, online retail, etc. are adapting to the reality of IoT to leverage its advantages. Machine learning technology is constantly evolving and the current trends in the field promise that every enterprise will be data driven and will have the capacity of using machine learning in the cloud to incorporate artificial intelligence apps. Companies will be successful in analyzing large complex data and providing meticulous insights without spending a huge amount on installing and maintaining machine learning systems.
Yesterday, tech giant Google announced its latest solution, the Cloud AutoML, that will enable developers, even those that lack machine learning expertise, to build image recognition models. It is said to be a part of the company's initiative to democratize AI learning and provide a simple approach that anyone can easily understand. "Our goal was to lower the barrier of entry and make AI available to the largest possible community of developers, researchers and businesses," Fei-Fei Li, Google Cloud AI chief scientists, and Jia Li, Google Cloud AI Head of R&D, wrote in the company blog. According to the duo, their latest solution would help businesses with limited machine learning expertise build "their own high-quality custom models by using advanced techniques like learning2learn and transfer learning from Google." The two believe that Cloud AutoML will make experts in artificial intelligence more productive and take the technology to greater heights while helping less-skilled engineers build more powerful machine learning systems.
Artificial intelligence (AI) is already impacting our lives in many ways. From intelligent video curation on Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) YouTube and Google web search to Apple's (NASDAQ:AAPL) Siri personal assistant, AI is already making our lives easier. AI can also help corporations and customers fight against rapidly evolving cyberthreats. For instance, FireEye's (NASDAQ:FEYE) Helix cybersecurity platform is able to automate threat detection and prevention with the help of this emerging technology. The early adoption of AI by Alphabet, Apple, and FireEye could help them steal a march over rivals.
Cloud machine learning platforms, sometimes referred to as machine learning as a service (MLaaS) solutions, can help make artificial intelligence (AI) affordable. But experts say enterprises and small businesses considering these services should also consider the potential challenges of these services before rushing in. Machine learning (ML), the branch of artificial intelligence concerned with creating computer systems that can learn without being explicitly programmed, is experiencing an undeniable boom. In its Technology, Media and Telecommunications Predictions, 2018, Deloitte Global wrote, "In 2018, large and medium-sized enterprises will intensify their use of machine learning. The number of implementations and pilot projects using the technology will double compared with 2017, and they will have doubled again by 2020."
For companies that are trying to figure how to take advantage of newer technologies like artificial intelligence (AI), but do not have adequate technical know-how or enough financial muscle to do so, Google Inc. believes its newly-launched automated tool, Cloud AutoML, might just provide an answer. Machine Learning (ML) is a subset of AI, and Cloud AutoML, according to Google's website, is a suite of ML products that enables software developers with limited expertise to train high-quality models by taking advantage of Google's proprietary image recognition and other learning technologies. "Currently, only a handful of businesses in the world have access to the talent and budgets needed to fully appreciate the advancements of ML and AI. And if you're one of the companies that has access to ML/AI engineers, you still have to manage the time-intensive and complicated process of building your own custom ML model," noted Fei-Fei Li, chief scientist, Cloud AI, and Jia Li, head of research and development (R&D), Cloud AI at Google in a joint blog post on 17 January. Cloud Auto ML, according to Google, is aimed at helping businesses "with limited ML expertise" to custom-build quality ML models by using "advanced Google techniques like learning2learn and transfer learning".
Google just announced significant enhancements to its machine learning services (MLaaS), attempting to close the significant competitive gap that Microsoft has enjoyed, in my opinion, for the last year or so. Not to be left out, Amazon.com AWS announced the company's own new MLaaS tools and services at AWS Re:Invent last November, trying to court AI application developers to build their smart apps on the AWS cloud. MLaaS is still in its infancy today, but it may become a dominant AI platform for enterprises who would prefer to leave all the messy details to someone else, and rent AI services by the click. This article summarizes each company's strategies and tactics and tries to size up the winners and losers.
Google just made it a lot easier to build your very own custom AI system. A new service, called Cloud AutoML, uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images. The technology is limited for now, but it could be the start of something big. Building and optimizing a deep neural network algorithm normally requires a detailed understanding of the underlying math and code, as well as extensive practice tweaking the parameters of algorithms to get things just right. The difficulty of developing AI systems has created a race to recruit talent, and it means that only big companies with deep pockets can usually afford to build their own bespoke AI algorithms.