The portion of marketers using AI to connect with customers is growing, a new survey shows, even though few are satisfied with the ability to balance personalization tools with privacy. In 2018, 29 percent of marketers used AI, according to the fifth edition of the Salesforce State of Marketing report, which surveyed more than 4,100 marketing leaders worldwide. By comparison, just 20 percent of marketers used AI in 2017. The 2018 AI adoption rate was higher, at 40 percent, among "high-performing" marketers -- those who said they are completely satisfied with their overall marketing performance and the outcomes of their marketing investments. Also: Can humans get a handle on AI?
At Amazon Web Services (AWS), we are continually innovating with new services and solutions. That's why we're excited to announce several new offerings from AWS Training and Certification to help AWS Partner Network (APN) Partners build new cloud skills and learn about the latest AWS services. Dive deep into the same ML curriculum we use to train Amazon's developers and data scientists. Choose from four role-based learning paths, with more than 30 digital ML courses and hands-on labs totaling 45 hours of training. Take our new AWS Certified Machine Learning – Specialty beta exam.
Across North America, CIOs and CTOs have begun to deploy artificial intelligence (AI) pilot projects. While some leaders have already moved to production deployments, many others have yet to advance beyond the initial consideration phase. Having the required AI knowledge and skills is a key factor, according to recent market studies. The biggest pain point that emerged from the Gartner 2018 CIO survey was the lack of specialized skills in AI, with 47 percent of CIOs reporting that they needed new skills for their AI projects. As such, IT talent development and knowledge transfer will be one of the biggest barriers to AI adoption going forward.
A creepy medical tool developed by Amazon uses artificial intelligence to spot whether you are ill long before your doctor. The project, called Amazon Comprehend Medical, scans through your medical record for key data points and then tells its medical professional customers what look out for. This means it can detect patterns in the data much faster than a doctor, suggesting diagnoses for underlying conditions. The use of health records by companies - now a market worth $3.2 trillion (£2.5tn) - has been heavily criticised over privacy concerns. However, developers say the data processed by the firm's algorithms is encrypted and will only be seen by customers and not shared with Amazon Web Services.
A creepy medical tool developed by Amazon uses artificial intelligence to spot whether you are ill long before your doctor. The project, called Amazon Comprehend Medical, scans through your medical record for key data points and then tells medics what illnesses to look out for. This means it can detect patterns in the data much faster than a doctor, suggesting diagnoses for underlying conditions. The use of health records by companies - now a market worth $3.2 trillion (£2.5tn) - has been heavily criticised over privacy concerns. However, developers say the data processed by the firm's algorithms is encrypted and will only be seen by customers and not shared with Amazon Web Services.
Manufacturing enterprises are quickly deploying AI solutions to stay ahead, but how to do scale these advances -- and where to begin -- remain elusive. This talk, moderated by Levatas' head of Data Science, will walk through how to perform human-in-the-loop analysis of unstructured data such as imagery and video footage, and how it could save businesses time and money. Using real examples in NLP and computer vision from other industries, you'll see how it could be possible for your firm to take advantage of these cost-saving technologies in the near-future. We'll walk through what's needed and what kind of results other industries are seeing and what the potential is for this industry. Daniel is an avid technology enthusiast with 15 years of experience designing and architecting software applications.
As a PR pro, what should you know about AI's impact on comms? Whether your brand is leveraging AI already, your target consumers are already engaging with it every day, through tools like Siri, Alexa, Netflix, or Google Maps. Artificial intelligence has the potential to save your company massive amounts of time and money, making your entire go to market process more efficient. But what's the best way to incorporate AI into you comms strategy? Take 6 minutes to absorb our latest video, and get critical insight from Paul Roetzer, founder of the Marketing Artificial Intelligence Institute, as he answers all the questions you may have about AI's impact on PR and marketing.
In the last couple of decades, the traditional symbolic approach to AI and cognitive science -- which aims at characterising human intelligence in terms of abstract logical processes -- has been challenged by so-called connectionist AI: the study of the human brain as a complex network of basic processing units . When it comes to human language, the same divide manifests itself as the opposition between two principles, which in turn induce two distinct approaches to Natural Language Processing (NLP). On one hand Frege's principle of compositionality asserts that the meaning of a complex expression is a function of its sub-expressions, and the way in which they are composed -- distributionality on the other hand can be summed up in Firth's maxim "You shall know a word by the company it keeps". Once implemented in terms of concrete algorithms we have expert systems driven by formal logical rules on one end, artificial neural networks and machine learning on the other. Categorical Compositional Distributional (DisCoCat) models, first introduced in , aim at getting the best of both worlds: the string diagrams notation borrowed from category theory allows to manipulate the grammatical reductions as linear maps, and compute graphically the semantics of a sentence as the composition of the vectors which we obtain from the distributional semantics of its constituent words. In this paper, we introduce basic anaphoric discourses as mid-level representations between natural language discourse on one end -- formalised in terms of basic discourse representation structures (DRS) ; and knowledge queries over the Semantic Web on the other -- given by basic graph patterns in the Resource Description Framework (RDF) . We construct discourses as formal diagrams of real-valued matrices and we then use these diagrams to give abstract reformulations of NLP problems: probabilistic anaphora resolution and question answering.
The key to making better business decisions is surprisingly simple: take a proactive approach to using data. Every company gathers data in one form or another, but the way a company uses its data has a lasting impact on the ability to compete, innovate, and attract talent. For many companies and their employees, data is gathered and handled reactively. They collect and use data intermittently on an as-needed basis, but it's seldom collected for historical analysis to support the creation of AI solutions. Data-driven companies believe that being proactive with their data is the ultimate differentiator.
Artificial intelligence (AI) has been touted to enable digital transformation across industries. However, applying AI to specific business problems remains elusive for executives. To capitalize on the massive opportunities AI can offer, innovative companies have to embrace AI-driven cognitive automation. In this webinar, we will explore the impact of AI on decision making and how it can bring unprecedented business value to an organization.