Internet of Things News Thursday, April 26 Smart speaker, Iot data sources, Machine learning & more…

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What’s new?

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IoT devices could be next customer data frontier

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  • At the Adobe Summit this week in Las Vegas, the company introduced what could be the ultimate customer experience construct, a customer experience system of record that pulls in information, not just from Adobe tools, but wherever it lives.
  • In fact, they spent $6.5 billion dollars last week to buy MuleSoft to act as a data integration layer to access customer information from across the enterprise software stack, whether on prem, in the cloud, or inside or outside of Salesforce.
  • There are very likely a host of other categories too, and all of this information is data that needs to be processed and understood just like any other signals coming from customers, but it also has unique characteristics around the volume and velocity of this data it is truly big…
  • Part of what Adobe, Salesforce and others can deliver is a way to gather that information, pull it together into his uber record keeping system and apply a level of machine and learning and intelligence to help further the brands ultimate goals of serving a customer of one and delivering…
  • The same could be said for the other IoT data sources, the car and sensor tech, or any other connected consumer device.

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Machine Learning, Artificial Intelligence to Transform Retail

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  • For example, predictive analytics can give us insights about future results, while prescriptive analytics can tell us the decisions we need to make today so that were more likely to see results we want.Today, were creating machine-learning algorithms to help retailers incorporate all of these sources of data and solve…
  • Thats a logistical and forecasting challenge that cant be ignored, and its one area where data science can begin to provide solutions.WWD: What obstacles might retailers have to overcome as they incorporate these new data analytics approaches?S.A.: These new systems are only as good as the data they interpret.
  • Retailers are already collecting a great deal of data every hour, on everything from online and in-store customer interactions, purchases and returns, to product attributes and inventory information and so on.
  • Many are exploring new sources of data that can add additional dimensions, such as in-store sensors, connected internet-of-things devices and data flows from social networks.But all of this data will be worth nothing in the future unless retailers are able to generate predictive and prescriptive information from it, [and] then…
  • For example, if you lack detailed product attribute information, you may not be able to understand the decisions your customers might be making for example, why someone who ordinarily buys Product A might decide to substitute Product B instead.WWD: What are some other challenges AI might help solve?S.A.: Were not…

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Difference of Data Science, Machine Learning and Data Mining

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  • To put it more simply, data mining is a set of various methods that are used in the process of knowledge discovery for distinguishing the relationships and patterns that were previously unknown.
  • We can therefore term data mining as a confluence of various other fields like artificial intelligence, data room virtual base management, pattern recognition, visualization of data, machine learning, statistical studies and so on.
  • Data mining is thus a process which is used by data scientists and machine learning enthusiasts to convert large sets of data into something more usable.
  • Machine learning and data mining follow the relatively same process.
  • However, unlike machine learning, algorithms are only a part of data mining.

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The Artificial Intelligence (AI) as a service market is expected to grow at a Compound Annual Growth Rate (CAGR) of 48.2%

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  • The growing demand for AI-powered services in the form of Application Programming Interface (API) and Software Development Kit (SDK) and increasing number of innovative startups are some of the major factors that are driving the growth of the AI as a service market.
  • Verticals in the AI as a service market include Banking, Financial Services, and Insurance (BFSI), healthcare and life sciences, retail, telecommunications, government and defense, manufacturing, energy, and others (Education, Agriculture, Transportation, and Media and Entertainment).
  • Factors such as the rapid generation of large volumes of data, due to the growing use of digital technologies, Internet of Things (IoT), and connected devices, coupled with the growing need to reduce operational costs are expected to fuel the growth of the AI as a service market across regions….
  • By Company Type – Tier 1 18%, Tier 2 46%, and Tier 3 36% – – By Designation C-level 24%, Director-level 51%, and Others 35% – – By Region North America 25%, Europe 46%, APAC – 16%, and MEA – 13% – – – – Major vendors in the global…
  • Research Coverage – – The AI as a service market has been segmented on the basis of service types (software tools, and services), technologies, organization size, verticals, and regions.AI as a service helps; venture capitalists and angel investors; Information Technology (IT) management directors/managers; government organizations; research organizations; consultants/advisory firms; IT…

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[/vc_column_text] [vc_column_text el_class=”topfeed-embedly”] The Artificial Intelligence (AI) as a service market is expected to grow at a Compound Annual Growth Rate (CAGR) of 48.2% [/vc_column_text] [/vc_column] [/vc_row] [vc_row el_id=”www_thecipherbrief_com_artificial_intelligence_welcome_age_disruptive_surprise_”] [vc_column width=”1/2″] [vc_separator] [vc_column_text]

Artificial Intelligence: Welcome to the Age of Disruptive Surprise

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  • The difference between warning and forecasting plagues discussion about artificial intelligence.
  • Our leaders decision-making in economics, law enforcement, and warfare will be accelerated by artificial intelligence, eventually being accelerated to a point where humans often will stand aside.
  • When weighing the current pace of advances in autonomous systems, machine cognition, artificial intelligence, and machine-human partnership, here is the very short list of things I can forecast with confidence: – – As the visionaries and practitioners argue about what AI will and wont be able to do, no controversy…
  • An artificial intelligence that is conscious is a good subject for warning, but a poor subject for forecasting, at least until we have a better notion of what consciousness is.
  • As I watch the development of AI, there are three particular breakthroughs that I am watching for…three milestones in cognition that may foreshadow human-level reasoning: – – Warning, forecasting, and tracking indicators will be vital in harnessing this very disruptive technology.

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