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) …
Back in May, I asked whether AI should assist in surgical decision-making. Following a recent venture round, investors seem to be voting yes as a company called Activ Surgical announces a $15 million raise. Activ rolled out an AI/ML platform called ActivEdge, which is designed to provide real-time intelligence and visualization to surgeons. The platform and its associated products will be initially available in the U.S. market, with plans to commercialize globally in 2021. The goal of integrating AI decision making with human surgical capabilities is to reduce medical errors.
As the insurance sector competes to win market share, Henry Jinman at EBI.AI discusses three ways companies can benefit from the power of Artificial Intelligence The UK general insurance market continues to be fiercely competitive. While the battle for repeat business keeps downward pressure on pricing, a constantly changing regulatory agenda increases costs. Whatever the industry, successful companies know that building a business based on price alone is not sustainable. Customer service is what matters most. It's a sentiment that is reflected in the latest findings of multinational professional services company Ernst & Young (EY).
"Business Insider reports that over 80% businesses are expected to have Chatbot automation implemented by 2020, given its efficiency and versatility. Chatbots are on rising and gaining lot of popularity! The progress in NLP -- Natural Language Processing -- field is scaling new heights everyday and with that Natural Language Understanding of machines is increasing tremendously which in-turn increasing capabilities of Chatbots. NLP based chatbots are better at understanding the question it is asked and providing answer. Not only that, it can serve customers 24/7 and on multiple channels like Google Assistant, Facebook Messenger, Your website, etc.
Marketing Artificial Intelligence Institute announced the launch of AI Academy for Marketers, an online education platform that helps marketers understand, pilot and scale artificial intelligence. AI Academy for Marketers is designed for marketing professionals and students at all levels, and largely caters to non-technical audiences, meaning registrants do not need backgrounds in analytics, data science or programming to understand and apply what they learn. The Academy features deep-dive Certification Courses (3 – 5 hours each), along with dozens of Short Courses (30 – 60 minutes each) taught by leading AI and marketing experts. The courses are complemented by additional exclusive content, including: live monthly Ask Me Anything sessions with instructors, the Answering AI series of quick-take videos that provide simple answers to common AI questions, keynote presentations from the Marketing AI Conference (MAICON), and AI Tech Showcase product demos from leading AI-powered vendors. New content will be regularly added to the platform, and all members get access to a private online community Slack group to foster collaboration and knowledge sharing with their peers.
Although the importance of machine learning methods in genome research has grown steadily in recent years, researchers have often had to resort to using obsolete software. Scientists in clinical research often did not have access to the most recent models. This will change with the new free open access repository: Kipoi enables an easy exchange of machine learning models in the field of genome research. The repository was created by Julien Gagneur, Assistant Professor of Computational Biology at the TUM, in collaboration with researchers from the University of Cambridge, Stanford University, the European Bioinformatics Institute (EMBL-EBI) and the European Molecular Biology Laboratory (EMBL). "What makes Kipoi special is that it provides free access to machine learning models that have already been trained," says Julien Gagneur.
Terms like'Data Science', 'Machine Learning', and'Data Analytics' are so infused and embedded in almost every dimension of lifestyle that imagining a day without these smart technologies is next to impossible. With science and technology propelling the world, the digital medium is flooded with data, opening gates to newer job roles that never existed before. However, quite often it is witnessed that beginners get confused over similar terms being used interchangeably, like'Data Science' and'Data Analytics'. This post will give you a clear idea about what some of the prominent concepts and job roles in Data are, and how they differ from each other! The most popular field that has emerged in the wake of digital disruption is'Data Science'. Data being oil and fuel of all the operations, companies are making the most of the accessible data that had never been used before.
"In the era of big data, insights collected from cloud services running at the scale of Azure quickly exceed the attention span of humans. It's critical to identify the right steps to maintain the highest possible quality of service based on the large volume of data collected. In applying this to Azure, we envision infusing AI into our cloud platform and DevOps process, becoming AIOps, to enable the Azure platform to become more self-adaptive, resilient, and efficient. AIOps will also support our engineers to take the right actions more effectively and in a timely manner to continue improving service quality and delighting our customers and partners. This post continues our Advancing Reliability series highlighting initiatives underway to keep improving the reliability of the Azure platform. The post that follows was written by Jian Zhang, our Program Manager overseeing these efforts, as she shares our vision for AIOps, and highlights areas of this AI infusion that are already a reality as part of our end-to-end cloud service management."--Mark
Tencent unveiled a comprehensive blueprint for the development of its Artificial Intelligence technologies at the 2020 World Artificial Intelligence Conference (WAIC) in Shanghai. Tencent introduced a white paper, "Tencent AI: Ambient Intelligence", and announced the launch of the Light 2.0 Program and four new platforms including an AI pan-entertainment platform, an AI console for broadcasting and TV media, a content review platform, and an industrial AI platform. With the launch of a comprehensive and open AI ecosystem, Tencent is seeking to further unlock the value in the AI industry, reinforce the company's leadership in AI, and kickstart a new round of innovation in AI. The Light 2.0 Program and four new platforms aim to unlock the benefits of accumulated technologies and capabilities, inspire and train young AI talent for tomorrow, and speed up the integration of AI with industries. The program, which follows from its predecessor Light 1.0 in 2019, takes advantage of technologies, products, resources, and projects from Tencent and its partners in the area of AI. It also provides a communication and experimental platform that promotes cooperation between industry and higher education and facilitates the training and development of future leaders.
Every company will become an ML company. In the world of Harry Potter, the sorting hat serves as an algorithm that takes data from a student's behavioral history, preferences and personality and turns that into a decision on which Hogwarts house they should join. If the real world had sorting hats, it would take the form of machine learning (ML) applications that make autonomous decisions based on complex datasets. While software has been "eating the world," ML is starting to eat software, and it is supercharging trillion-dollar global industries such as healthcare, security and agriculture. If ML is expected to create significant value, the question becomes: where will this value accrue?
Using data to build claims reports to measure the health of the business was previously a tedious two-step process for frontline staff of Suncorp Group's insurance business. Speaking to ZDNet, Suncorp head of automation Tim Johnson explained how historically the process required staff to approach the business insights team to help produce the reports and find any anomalies. This process often took up to two weeks and limited their ability to react quickly to change. "What we had was a very, very strong foundation and data lake for probably the last five years … its been really good. But we were still struggling to get ... sort of flexible analytics to the frontline where the decisions were made," he said.