Call center software provider Five9 Inc. has come up a winner yet again, comfortably beating Wall Street's targets with its third-quarter financial results and delivering strong guidance on top of that. The company reported a profit before certain costs such as stock compensation of 27 cents per share on revenue of $112 million, up 34% from a year ago. That was well ahead of Wall Street's forecast of 18 cents per share in earnings and $101 million in revenue. Five9 sells cloud-based contact center software and services for enterprises that enable them to keep track of and manage their interactions with customers. Its software covers traditional phone calls, as well as video calling services, emails and social media interactions.
Amazon Australia has announced plans to open a second fulfilment centre in Melbourne, Victoria late next year. With construction in Ravenhall, Melbourne already underway, the new centre, according to the global e-commerce giant, will more than double the company's footprint in Victoria. "Our investment in this new Melbourne fulfilment centre will benefit customers around Victoria, while creating hundreds of jobs for Melbournians in a safe work environment, with competitive pay at a time when they are needed most," Amazon Australia director of operations Craig Fuller said. "This fulfilment centre will also provide additional capacity for Victorian-based small and medium-sized businesses who utilise the Fulfilment By Amazon service to benefit from our expanded capability and seamlessly serve customers across the country." When completed, the new centre will be 37,000 square metres, with capacity to house up to six million items from its online store.
Google blasted through the coronavirus pandemic with gangbuster earnings, just a week after U.S. prosecutors sued the company for operating a purported illegal monopoly in its flagship search business. Alphabet Inc. reported a third-quarter profit of $11.2 billion, well outstripping analyst estimates. As importantly, digital advertising revenue of $37.1 billion was up compared with last year, marking a turnaround from a quarter earlier, when the company recorded the first drop in the category in company history. Cogs across the Alphabet empire were clicking. Helped by stay-at-home trends, YouTube pulled in more than $5 billion in advertising for the first time, gaining 32% over the same period a year earlier.
Apple may be stealthily developing its own search engine, as Google faces a lawsuit from the U.S. antitrust authorities regarding the search engine giant's agreements with companies to be the default search tool. In the newest operating system update for the iPhone, the iOS 14, Apple has started showing its own search results and direct links to websites when users search from their home screen. In its updated version, iOS 14 does not use Google for many of its search functions, as it previously used to. The search window that appears in iPhones when users swipe right now compiles Apple-generated search suggestions rather than Google results. Earlier this week, the U.S. Department of Justice, in a landmark lawsuit said, Google is monopolizing the search space by entering into multi-billion dollar deals with mobile companies like Apple, Motorola, and network carriers like AT&T and Verizon, to be the default search engine on devices.
Google has had an eventful couple of weeks, announcing enhancements to its search and map capabilities at its virtual "Search On" event on Oct. 15, and on Oct. 20 being accused by the US Justice Department of engaging in anti-competitive practices in order to preserve its search engine business. At the Search On event, Google detailed how it has tapped AI and machine learning techniques to make improvements to Google Maps as well as Search. In an expansion of its search "busyness metrics," users will be able to see how busy locations are without identifying the specific beach, grocery store, pharmacy or other location. COVID-19 safety information will also be added to business profiles across Search and Maps, indicating whether the business is using safety precautions such as temperature checks or plexiglass shields, according to an account in VentureBeat. An improvement to the algorithm beneath the "Did you mean?" features of search, will enable more accurate and precise spelling suggestions.
The ability of computers to autonomously learn, predict, and adapt using massive datasets is driving innovation and competitive advantage across many industries and applications. The artificial intelligence (AI) is budding faster and prompting businesses to hop aboard the next big wave of computing to uncover deeper insight, quickly resolve their most difficult problems, and differentiate their products and services. Whether the goal is to build a smarter city, power an intelligent car, or deliver personalized medicine, we've only just begun to understand the real potential of AI. For the implementation of AI, HPE OEM has the expertise, edge to core technologies and partner ecosystem to help explore different use cases, experiment with AI and data technologies, and build the solution to be enterprise-ready. HPE OEM will benefit at all stages of the journey from formulating a roadmap through implementation and data migration.
It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.
If artificial intelligence is going to spread to trillions of devices, those devices will have to operate in a way that doesn't need a human to run them, a Google executive who leads a key part of the search giant's machine learning software told a conference of chip designers this week. "The only way to scale up to the kinds of hundreds of billions or trillions of devices we are expecting to emerge into the world in the next few years is if we take people out of the care and maintenance loop," said Pete Warden, who runs Google's effort to bring deep learning to even the simplest embedded devices. "You need to have peel-and-stick sensors," said Warden, ultra-simple, dirt-cheap devices that require only tiny amounts of power and cost pennies. "And the only way to do that is to make sure that you don't need to have people going around and doing maintenance." Warden was the keynote speaker Tuesday at a microprocessor conference held virtually, The Linley Fall Processor Conference, hosted by chip analysts The Linley Group.
AI has already begun to automate many non-mission critical business processes, including aspects of customer service and human resources. As the technology advances, new opportunities continue to emerge, in particular AI's ability to automate the movement to, and management of mission-critical workloads on hybrid cloud environments. Many businesses--especially those in highly regulated industries such as telecom, financial services and healthcare--are hesitant to move mission-critical workloads to the cloud. In fact, data from multiple sources reveals that only 20 percent of all workloads have moved to the cloud. Businesses further along in their journey understand the benefits of cloud use and often have already turned to the cloud for non-mission critical workloads. The accelerated proliferation of mission-critical applications--combined with the fact that more than 70 percent of organizations using public cloud are working with multiple vendors--means companies must approach the migration of these applications to a hybrid cloud environment using a four-phased approach: advise, move, build and manage.
Reinforcement Learning is a subset of machine learning. It enables an agent to learn through the consequences of actions in a specific environment. It can be used to teach a robot new tricks, for example. Reinforcement learning is a behavioral learning model where the algorithm provides data analysis feedback, directing the user to the best result. It differs from other forms of supervised learning because the sample data set does not train the machine.