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.
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.
There are a plethora of success stories demonstrating how major financial players capitalise on their data. The coronavirus pandemic and the global measures that have followed have created a perfect economic storm. The financial sector stands at the front line of a growing credit crisis, with banks trying to manage disruption and maintain strict compliance amid social distancing guidelines which are at odds with their processes. Then there are the extraordinarily low interest rates and increasingly cash-insecure consumers to contend with. To navigate the immediate obstacles, financial institutions must assess short-to-medium-term financial risks and adapt to new ways of operating in a post-pandemic world.
It's no secret that influencer marketing is booming. No matter which social channel you turn to, influencers have cultivated a steady presence and loyal audiences -- and brands are noticing. In fact, 63% of marketers are planning to increase their influencer marketing spend over the next year alone. While influencer marketing has been expanding for many years, 2020 will be remembered as the year that solidified its role in the marketing mix. We saw newcomers like TikTok explode to mainstream popularity, adding a new batch of rising stars to the rich influencer ecosystem rooted in platforms like YouTube, Instagram, and Twitch.
GPT-3, an advanced language-processing artificial intelligence algorithm developed by OpenAI, is really good at what it does -- churning out humanlike text. But Yann LeCun, the Chief AI Scientist at Facebook who's been called a "godfather of AI," trashed the algorithm in a Tuesday Facebook post, writing that "people have completely unrealistic expectations about what large-scale language models such as GPT-3 can do." LeCun cites a recent experiment by the medical AI firm NABLA, which found that GPT-3 is woefully inadequate for use in a healthcare setting because writing coherent sentences isn't the same as being able to reason or understand what it's saying. "It's entertaining, and perhaps mildly useful as a creative help," LeCun wrote. "But trying to build intelligent machines by scaling up language models is like [using] high-altitude airplanes to go to the Moon. You might beat altitude records, but going to the Moon will require a completely different approach."
Everyone has their own definition of what Artificial Intelligence is. In its most basic form, AI is simply our attempt to replicate human intelligence in machines. We program computers to play chess and drive cars, and not at the same level as humans, but better. Although we think of AI as something that only scientists at MIT have access to, it is actually something that is being integrated into businesses all over. Whether it is to analyze consumer trends, predict future demand, recommend personalized content or power customer chatbots, there is an AI solution for it all.
Prominent growth areas include virtual agents in banking, infotainment systems in automotive, managing vast amounts of data and tele-health in healthcare, and education, which has seen a significant switch to online learning due to pandemic-related social distancing. Also, the number of IoT devices at home and in the workplace will multiply exponentially, with the number of active devices increasing from 7.6 billion at the end of 2019 to an estimated 24.1 billion in 2030. In the more immediate future, it's estimated that by the end of 2020, 50% of all search will be conducted via voice and 75% of U.S. homes will have at least one smart speaker. Tech providers thus have a growing requirement for vast amounts of speech data upon which to base reliable and comprehensive services. They have to protect against fraud and impersonation, recognize dialects and accents, even identify a user's emotional mood to respond in the most appropriate manner.
It was August last year and I was in the process of giving interviews. By that point in time, I was already interviewing for Google India and Amazon India for Machine Learning and Data Science roles respectively. And then my senior advised me to apply for a role in Facebook London. Contacted a recruiter on LinkedIn, who introduced me to another one and my process started after a few days for the role of Machine Learning Engineer. Now Facebook has a pretty different process when it comes to hiring Machine learning engineers. They do coding rounds, system design, and machine learning design interviews to select future employees.
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The graph represents a network of 2,067 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 26 October 2020 at 12:02 UTC. The requested start date was Monday, 26 October 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 9-hour, 0-minute period from Thursday, 22 October 2020 at 14:58 UTC to Sunday, 25 October 2020 at 23:58 UTC.