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Do you have a complete, comprehensive, single version of the truth about your business?... - 0 views

www.autolearn.ai/...-the-truth-about-your-business autolearn business technology innovation tech digital strategy blockhain data
shared by pintadachica on 20 Dec 18 - No Cached
  • pintadachica on 20 Dec 18
     
    One of the key milestones on the Digital Journey starting with a Digital Strategy, Digital Transformation and then sustaining on Digital Innovation is the point where the enterprise reaches a point of data maturity powered by a single, organization wide, consistent version of the truth including the state of the customers and the state of the business and the state of the employees. This point is critical as it becomes the launchpad for several, forward looking initiatives including Artificial Intelligence, ChatBots, Blockchain etc. "Complete" A Complete version of the truth ensures that the following criteria is met: Entity Pivot The key entities that need to be tracked to generate a complete, comprehensive version of the truth are the following Employee Employees, regardless of customer facing or not, need to be understood including where they excel vs. struggle and where their struggle impact the customer experience. Key information about employees that should be tracked is what the employees are working on, how productive they are and how often they introduce delay and errors in business processes. Business Business visibility requires that the enterprise be able to track key metrics such as customer lifetime value, customer attribution, customer acquisition cost and customer satisfaction. In addition, the stage of the customer ranging from prospect to commit to paying customer to abandoned needs to be tracked. In addition, the customer's quality of service and experience needs to be tracked and understood. Customer The most critical of the three is the understanding of the customer. Customer KPIs have a direct impact on and are completely impacted by the Business and Employee KPIs. It is extremely important to understand how customers are searching for, discovering, learning, understanding, using and continuing to use the product and services delivered by the enterprise. In addition, it is important to understand what capabilities drive what kin
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Can you transform into a tech company? - The AI Company - 0 views

www.autolearn.ai/...-transform-into-a-tech-company autolearn AI artificial intelligence digital strategy Company auto learn
shared by pintadachica on 11 Nov 18 - No Cached
  • pintadachica on 11 Nov 18
     
    Transforming into a tech company has become top of mind for executives in all major industries. It is clear that modern technology will fundamentally alter what and how business is done in every domain, sector, and industry. This has led to a call to arms in every enterprise to understand how they can transform into a tech company. The Tech Company Magic Tech companies have fine-tuned the art of bring new digital products and services to the market, quickly, efficiently and effectively and understanding customer feedback to iterate and improve. This capability makes them incredibly agile and leads to faster experimentation that is cheaper and involves less risk. In turn, this enables them to bring new capabilities to the market and even if all do not succeed or get traction, a few do and that drives innovation, customer satisfaction, and growth. From the outside, tech companies appear to be massive juggernauts that are unstoppable and able to crush everything in their path. The 'Non-Tech' Technology has been leveraged in every sector and industry, however, it has almost always been treated as a means to an end, something that is required but never the real value driver for the customer. This has led to the typical organizational structure in enterprises into "Business", "Operations" and "Information Technology". The "Business" arm generates value for customers, the "Operations" team carries out the requirements of the Business team and the "Information Technology" team provides the systems (databases, network and compute) required to "keep the lights on" for the Operations and Business Teams" This structure served enterprises well in the last decades as customers did not have an alternative to directly working with the enterprise and this fortified the value supply chain and also established a hierarchy of sorts within the enterprise where the business looked down upon operations who looked down upon technology. The purpose of
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Are You Leaving Money On The Table And Why A Monetization Strategy Is Key - The AI Company - 0 views

www.autolearn.ai/...you-leaving-money-on-the-table artificial intelligence autolearn innovation technology digital strategy enterprises monetization
shared by pintadachica on 07 Jan 19 - No Cached
  • pintadachica on 07 Jan 19
     
    Enterprises across the board have a lot of untapped potential in their data. The data is not only relevant and useful within the enterprise but can be a valuable source of insights for the enterprise partners and customers. In some cases, the value of this data can be high that partners and customers are willing to pay extra to get access to this information at a certain fidelity, freshness or scope. Enterprises that do not have a clear and coherent monetization strategy are leaving money on the table. In addition, they stand to lose customers to competitors who gain the first movers advantage by addressing this market need. The Value of Data The first step in determining a monetization strategy is an audit of the enterprise data assets and a determination of the customers who are interesting and willing to pay a premium for access to this data. The Value of Data is proportional to the following: Freshness The more "fresh" a dataset is higher its value typically. This is because there is an advantage in the early visibility provided by first access to new information. 'Freshness' is defined the latency between the creation of data and the delivery of the data to the consumer. Consumers of data will pay a premium for fresh data if it fits into their decision and action strategy. Fidelity Higher the "fidelity" of data i.e. how much detail a particular data point carries also increases the value of the data in the eyes of the data consumer. Higher fidelity data offers more information and detail enabling the consumer to design highly valuable analysis that leverages the additional details offering a deeper insight into the situation at the present or historically. Raw The more "raw" a data set, higher its value as it can support a much larger set of analysis scenarios that a processed data set could support. Data sets that are aggregated, sampled, filtered or transformed can have a lower value as they can severely limit the type of analysis. Raw
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APIS ARE DEAD, LONG LIVE APIS - 0 views

www.autolearn.ai/...is-are-dead-long-live-the-apis autolearn artificial intelligence innovation digital strategy transformation tech AI
shared by pintadachica on 13 Dec 18 - No Cached
  • We believe that APIs are about to enter the second growth spurt. APIs will evolve from not just interfaces and integration enablers into the rockets that propel enterprises towards innovation and market dominance. Here are three key trajectories that will lead the next API evolution and revolution.
  • pintadachica on 13 Dec 18
     
    Modern, RESTful APIs are not considered standard, table stakes and expected out of any new project, effort, application, system, service or product. It has become so normal to talk about developer interfaces, developer adoption, application development and innovation in the same breath as APIs that a distinct effort to build APIs for a new product or service seems out of place and abnormal. APIs are the defacto standard of app development. So where do we go from here? We believe that APIs are about to enter the second growth spurt. APIs will evolve from not just interfaces and integration enablers into the rockets that propel enterprises towards innovation and market dominance. Here are three key trajectories that will lead the next API evolution and revolution. Innovation - Starts, and Ends with APIs All modern technologies such as Artificial Intelligence, Machine Learning, ChatBots, Analytics, BlockChain etc. begin and end their stories with APIs. APIs are what enables the communication between front-end user interfaces and the backend technology services. All new machine learning capabilities offered out of the big four tech companies have seen the light of day through APIs. Intent & Sentiment extraction, Topics, Categories, Summarization, Image Recognition, Entity Extraction etc. are all capabilities powered by Machine Learning, Natural Language Processing that is ultimately being delivered as APIs to application developers. Similarly, ChatBots are typically designed to get the user entered text, use an intent API to determine intent and then use a service API to respond to the user conversationally or with a service. Clouds - Multi-Cloud, Hybrid Cloud As the big three cloud providers grow their market share and attempt to attract attention, increasingly, enterprises need to think about how they minimize their risk by building in the flexibility to switch their cloud provider if and when they need. In addition, hybrid architectures or a cloud migration
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Simplest Way Of Enabling An Administrator Account In Windows8 - Connect Writers and Fan... - 0 views

share-ask.com/...5095 way administrator account windows writers fans com
shared by Alice T on 22 Apr 13 - No Cached
  • Alice T on 22 Apr 13
     
    Frankly speaking, Microsoft's newest operating system has come with lots of new cool features that were previously non-existent in all the other previous versions of Windows. A good example is how Microsoft decided to move from the Start feature of the older versions to a whole new Metro Start feature. Some long term users of Windows 8 might require such an Administrator Account.In this article we are going to learn on how to enable the Administrator Account in Windows 8 because in real sense it is not missing but it is disabled.
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Future Proofing for Agility - The AI Company - 0 views

www.autolearn.ai/...2017-8-30-thoughts-on-agility Proofing agility Ai Company autolearn artificial intelligence strategy digital
shared by pintadachica on 11 Nov 18 - No Cached
  • pintadachica on 11 Nov 18
     
    A lot has been said about agility and the need for enterprises looking to innovate and disrupt to build agility. Agility, at the same time, also gets confused with the process of scrum. Small and large teams get enamored with the idea of scrum and mistake the process with the state of being agile. This is often more detrimental to the enterprise and can often create more process and not enough real agility. What is Agility Agility is the efficiency with which an enterprise executes and delivers on its objectives and goals. Agility is the ability to react to changes in goals, feedback from customers and shifts in strategy. Agility, from the outside, looks like a predictable stream of value delivered by the enterprise that matches and exceeds the needs of the customer. Organizational agility requires agility at multiple levels within the enterprise to drive the insights that can channel and align the efforts of the entire organization by leveraging data and information to make quick and informed decisions. Business Agility Business and customer-facing employees need to achieve "Business Agility". This is the ability of these employees to react to business critical in real time if needed and have access to the latest information at any decision point. Business Agility enables users to reduce the latency or lag between a need in the market or of the customers and when they are able to service the need. Decision Agility Analysts and data scientists creating the insights to drive decisions require "Decision Agility" i.e the ability to easily discover, leverage and use data for analytics and insights through any and multiple tools and channels. Analysts and data scientists need to produce insights that reduce the time and effort required to convert data into information and insights that are required to drive key decisions and actions. Development Agility Application developers and data engineers need the ability to easily generate, collect, access and deli
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Beware of Technology Congestion - The AI Company - 0 views

www.autolearn.ai/...2017-8-3-technology-congestion technology innovation AI company autolearn congestion digital journey
shared by pintadachica on 20 Dec 18 - No Cached
  • pintadachica on 20 Dec 18
     
    Technology Congestion is a not a recent phenomenon but the urgency around Digital Innovation and Digital Transformation has brought it front and center. Technology Congestion is a point in the Digital Journey where multiple technology initiatives, executed in parallel become entangled with each in a state where none of the initiatives, hampered by inter dependencies, prioritization, and cost, is able to complete, make progress and deliver business value. Modern Experiences Require Multiple Technologies Building a consumer driven, customer centric experience that truly delights and moves business KPIs requires several technologies to come together in almost a magical experience. This means that not on boarding and deploying multiple technologies is not an option or possibility. Enterprises have to build competencies in multiple technologies (and they have multiple strategic options to do so) and this can be a daunting task. Managing Technology Dependencies Often, an app-centric methodology requires a complete focus on the user and customer's experience. Delivering that experience can requires technologies that leverage each other or are inter-dependent on each other. Inter-dependencies can be sequential i.e. Technology A is required to be installed and operational before Technology B can be initialized. Inter-dependencies can also be matrixed i.e. a service X might require service Y to be complete and Service Y requires Technology B. Inter-dependencies can also be circular where System M feeds information into System N and System N, in turn, provides feedback to enable System M to iterate and improve. Innovation To A Screeching Halt Technology congestion can stall innovation. Sorting out dependencies can delay innovation and new product development and cause the enterprise to become anti-app-centric. The net impact is lost time and energy in technology installation and deployment with less than ideal focus and attention on customer value and user experience.
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Not too late to jump on Blockchains - The AI Company - 0 views

www.autolearn.ai/...oo-late-to-jump-on-blockchains autolearn technology digital strategy data business AI company design blockhains
shared by pintadachica on 20 Dec 18 - No Cached
  • pintadachica on 20 Dec 18
     
    It's not too late to jump on to the Blockchains trend. We often hear from customers whether it is too late to get behind blockchain. The number of startups in this space is growing rapidly and the number of large ISVs and SIs providing blockchain capability and expertise is also increasing. However, the market and the technology space for blockchains is far from saturated, settled or stable. Here are three reasons why now is a good time to get into blockchains Early Mover Advantage Blockchains are bound to disrupt fundamentally how business is done. By enabling trust in an otherwise trust-deficient environment, blockchains enable transactions between two or more enterprises who otherwise might not know about each other. Blockchains offer solutions to several common problems faced by the enterprise such as Digital Identity, Secure Data Storage, Secure Data Sharing, Distributed Ledger, Distributed Databases etc. Blockchains are based on strong cryptographic standards based in mathematics, cryptography, and encryption. Building blockchains as a core competency requires a deep understanding of the mathematics behind it and an internal process for deploying, managing and developing on top of the blockchain. Early mover advantage can be generated by taking small steps in this area and targeting simpler scenarios initially. Nascent Technology It is going to take another few iterations of the blockchain technology before it can truly be enterprise-ready. There is a lot of ongoing work to make blockchains more secure, scalable and performant. Innovations are constantly being made and added to the core blockchain technology that is constantly increasing the type and complexity of applications that it can support. As the technology matures, enterprises can be well suited and ready to leverage the advancements by building a core competency in not only the blockchain technology but by becoming a member of the blockchain community, standards and keeping up to date with th
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Are you worried about the quality of your data? - The AI Company - 0 views

www.autolearn.ai/...about-the-quality-of-your-data AI company autolearn Business innovation technology digital journey strategy data transformations
shared by pintadachica on 20 Dec 18 - No Cached
  • pintadachica on 20 Dec 18
     
    The quality or the lack thereof can be a huge contributing factor to a fractured and sluggish digital journey where ROI is hard to achieve and results come in short supply. The quality of data has a direct impact on the ability of the enterprise to be aware of relevant events, its reaction time, the decision time and its action time. A clear and concerted effort is required to measure and improve the data quality to drive better decisions and actions. Common Quality Issues The following are the most common quality issues Comprehensiveness Comprehensiveness quality issues refer to key attributes or data points missing from the data collected by the enterprise. This can occur when the data producing systems or the data delivery networks have glitches or malfunction or are incorrectly configured to miss entire rows of data or attributes of the data. Integrity Integrity quality issues refer to the corruption of the values of key attributes to contain unidentifiable or unreadable data. When key attributes are empty or null when they are by design, not allowed to be empty/null or when an attribute contains a value that does not meet the specifications of the type of the attribute for example, a string column contains an integer or a timestamp column contains a string not parse-able into a timestamp. Integrity of data is important before data can be included in the data set to drive analysis, decisions and actions. Sampling Sampling quality issues refer to the inclusion or exclusion of a certain percentage of the records in a data set with the assumption that the remainder records are good, representative sample of the original data set. Bad or inaccurate sampling can lead to a distorted view of reality and that can lead to bad decisions. In addition, sampling itself can make the data set inappropriate for certain types of analysis that require the entire dat set to be utilized for training. Filtering An upstream filtering scheme can end up removing too many or
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The Long & Short Of An AI Strategy - The AI Company - 0 views

www.autolearn.ai/...ort-term-vs-long-term-strategy auto learn disital strategy term AI company artificial intelligence
shared by pintadachica on 11 Nov 18 - No Cached
  • pintadachica on 11 Nov 18
     
    Much has and needs to be said about an enterprise's AI strategy. Artificial Intelligence or AI is considered a fundamentally disruptive technology similar to the steam engine, electricity etc, a technology that will be pervasive and absolute in its impact on the world and its inhabitants. The ability to find hidden patterns to predict the future or detect a behavior has massive implications across the world, in every industry, sector, and domain. When faced with this realization, enterprise's can find themselves stuck, paralyzed and unsure about how to proceed. The field of AI is decades old already and the early success stories have been practicing AI for multiple years already with the tech industry leading the way. How can an enterprise that has no experience and competency in this area let alone lead the technology or even leverage it appropriately to drive business value? When developing the AI strategy, two ideas are paramount. First, this a fundamentally disruptive technology and the enterprise will need to establish it as a core competency for the foreseeable future. Not doing so will not be an option. Second, a long-term plan to success is superseded by the need to drive quick wins and small successes not only to build confidence but use real-world experience to develop and hone that skill. The Short-Term AI Strategy The short-term AI strategy should focus on driving immediate business value through enhanced customer experiences that leverage any field of AI be it machine learning, deep learning, natural language processing etc. Driving the usage and deployment of AI in front of an end user making them smarter, productive and better informed can pay rich dividends by not only helping the enterprise can real-world experience, but it can also give a perception boost to the company as being innovative and cutting edge. However, most importantly, this can highlight and promote the success and potential of AI in the enterprise and encourage a snowball eff
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Platform Commoditization: How not to get sidelined by commoditization - The AI Company - 0 views

www.autolearn.ai/...on-how-not-to-get-commoditized technology innovation autolearn digital strategy journey platform commoditization AI
shared by pintadachica on 20 Dec 18 - No Cached
  • The Risk of Building Platforms: Cost of Marketing & Support
  • pintadachica on 20 Dec 18
     
    The cutting edge platforms for today will be the commoditized platforms of tomorrow. As the technology matures and evolves, the previous generation of technology becomes easier to build and deploy enabling a rush of vendors to capitalize on it by making it accessible to the largest possible customer base. This puts enterprises in the nontechnology sectors in an awkward position. Often not ready to consume the latest and greatest technology due to parts of their stack unable to leverage new technology and requiring upgrade to and deployment of the stepping stone technology, these enterprises have to choose between vendor lock-in in a multi-year software and service contract or risk building and implementing a version of the older technology in-house. Business Drivers of Infrastructure-as-a-Service The biggest risk in building technology platforms in-house is the risk of commoditization. The argument played out with the debate over internal vs. public clouds. Initially, enterprises were hesitant to leverage public clouds with several of them opting to build internal, private clouds. Building a cloud is hard. Operating and maintaining a cloud is even harder. Ensuring that the cloud is running on and leveraging the best in class technology requires dedication to the cause. This is often missing in non-technology enterprises by design given they are driven by different and separate business drivers and considerations. A cloud service provider is motivated to ensure the best in class service and technology because that drives revenue for them. An enterprise whose main business is not offering cloud or software services will not be motivated by the same drivers and thus there will be an inherent difference in their approach and success with building and delivering an internal cloud. Business Drivers for Platform-as-a-Service The same argument (public vs private clouds) applies to platforms. Building the best in class platforms that offer the ability to develop cuttin
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Self Preservation: The Number One Hurdle To Innovation - The AI Company - 0 views

www.autolearn.ai/...umber-one-hurdle-to-innovation innovation technology digital strategy tech AI artificial intelligence autolearn
shared by pintadachica on 07 Jan 19 - No Cached
  • pintadachica on 07 Jan 19
     
    One of the biggest hurdles to Digital Transformation and Digital Innovation is the organization's inertia and tendency to optimize for self-preservation. Self-preservation can exist in the enterprise at the individual, team, divisional or the organizational level and can have a devastating impact on the organization's ability to innovate and grow. Self-preservation is not a new phenomenon however, it is more deadly for an enterprise now than ever. This is because the speed of technology change has increased geometrically. In the past, self-preservation would automatically get corrected as the technology was generally learned and adopted slowly with enough time for the enterprise to become aware of the change and implement it. However, the rate of technology change has magnified tremendously and the enterprises no longer have the luxury to take their time with the change. Inaction risks getting left behind and other competitors who leverage and change faster stand to capture the largest market shares and customer mind share. Self preservation is the tendency of the enterprise to ignore, undermine or postpone the adoption and integration of new technology in the enterprise to avoid a change in the status quo across technology, products, services and most importantly, day to day operations and organizational structure. Self-preservation can lead to what is termed as "politics" in an org, it can stifle innovation and innovative individuals & teams and it can favor business driver stagnation over risk taking. 5 Signs of Self-Preservation The following are signs of self-preservation Highlighting the Journey of Innovation as Failure Adversarial teams and individuals within an enterprise who are interested in self-preservation often go out of their way to highlight the tough, risky journey of true innovation as a failure citing the cost and the time being taken to address the real problems in a truly innovative manner. While the individuals and teams trying to
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Why Is Sentiment Such A Big Deal? - The AI Company - 0 views

www.autolearn.ai/...2017-8-3-sentiment autolearn artificial intelligence AI company sentiment digital strategy technology
shared by pintadachica on 07 Jan 19 - No Cached
  • pintadachica on 07 Jan 19
     
    Sentiment, Sentiment Analysis, Sentiment Tracking have become a hot topic with multiple 'AI' startups focussing on providing sentiment driven insights to enterprises. The number of such startups points to the potential that enterprises see in Sentiment analysis and the impact it has on how the enterprise plans, operates, executes and delivers value. Sentiment Analysis is process of extracting sentiment (emotion or feelings) captured in signals that are embedded in various types of media such as print, text, audio, video, images etc. For example, if a reporter submits a report on a particular enterprise, the sentiment embedded in the article can point to how excited, worried, upbeat or impassive they might are about the enterprise. This sentiment can be then used by the enterprise to understand the perception about the enterprise that the external market carries and whether that perception is improving, degrading or staying unchanged. This insight can be used by the enterprise to improve their go to market plans, change their PR strategy or even go deeper and change their product strategy. Sentiment Analysis Is Not New The tracking, measurement and use of Sentiment is not a new scenario. Enterprises have been leveraging the output of sentiment analysis for a long time. User surveys, focus groups, market research, customer interviews etc. are all examples of generating data to perform and track sentiment. Similarly, influencer marketing through association with influencers or events or organizations with a certain perception or sentiment associated with them is a common technique to improve the enterprise's own sentiment. Sentiment Analysis and strategizing based on the analysis is a common and required function for any enterprise. Sentiment Analysis Using Artificial Intelligence With the advent of Artificial Intelligence (AI), enterprises now have another technique in their kitty to understand how they are perceived in the market and how that perception i
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To Create Is Not Enough: How to Focus on Consumption - The AI Company - 0 views

www.autolearn.ai/...gh-how-to-focus-on-consumption Artificial intelligence AI autolearn technology innovation digital strategy
shared by pintadachica on 18 Jan 19 - No Cached
  • pintadachica on 18 Jan 19
     
    A pitfall in the path to innovation and disruption is the lop sided focus on "creation" and not enough focus on "consumption". Creation is the process of creating platforms, products, and solutions where as consumption focusses on ensuring that the created artifacts deliver the intended value. Too often enterprises get caught up in "creation" or enabling "creation" and lose sight of the fact that without consumption, anything they create is bound to be a failure. Creation & Consumption can not be Sequenced A misconception that often exists is that "if we build it, they will come" i.e creating the product and solution is enough and its existence will automatically lead to consumption and value generation for the customer. However, creation and consumption cannot be sequenced i.e. made a focus sequentially. Creation and consumption only succeed when they go hand in hand where tight, iterative loops ensure that the creation is informed by consumption trends and feedback and that consumption is also leveraging the latest creations. What Does A Lop-Sided Focus Look Like The top 3 signs of this lop-sided focus are as follows Focus on Building Platforms When the focus of the enterprise is building platforms and when customer value is only created when a developer leverages the platform to build a customer facing application, it often means that the effort invested in the platform has no ROI. When the focus is on platforms, application investment suffers and the platform builders get sidetracked with platform KPIs as opposed to business value KPIs. The platform builders might only focus on "Developer" satisfaction when ultimately, customer satisfaction matters for the business. Focus on Tools, Not on Solutions Another sign of lop-sided investment is a focus on tooling at the expense of solutions. Enterprise teams can often go overboard with building large libraries of tools and using the adoption of the tool as a metric of success. However, simil
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Commentluv Blogs: 50+ Dofollow Enabled blogs List in 2019 - 0 views

www.bloggingden.com/commentluv-blogs Commentluv Dofollow Enabled blogs
shared by Satish Kumar Ithamsetty on 19 May 19 - No Cached
  • Satish Kumar Ithamsetty on 19 May 19
     
    The CommentLuv blogs are the websites that are useful in creating high-quality backlinks for your websites. CommentLuv blogging is one of the oldest techniques still being used by many bloggers. There are lots of people who follow CommentLuv blogging in the early days but kept its side due to technical updates on Google. CommentLuv Blogs ListEven though many people lost their trust on CommentLuv blogs there are still more people that are following this system to create high-quality backlinks and improve the domain authority. Still to this day meaning bloggers search for PR websites backlinks but the PR data is no more released by Google. You can search for high-quality domain authority blogs that allow the CommentLuv commenting system to generate backlinks. here I have written a quick article on how to find CommentLuv blogs and list of some CommentLuv blogs. Before knowing anything about CommentLuv enabled blogs, let us discuss something about the backlinking strategy.
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Skateboarding, The AI Company and the Autolearn Boost - The AI Company - 0 views

www.autolearn.ai/...en-skateboarding-autolearns-ai artificial intelligence innovation technology digital strategy AI company
shared by pintadachica on 09 Jan 19 - No Cached
  • What is common between skateboarding and learning to skateboarding & autolearn.ai’s AI platform. Lots, turns out. Consider the process of learning to skateboard. One repeatedly tries a move with the skateboard. You look at if you can land the move. If you do, you try a different move. if you don’t, you slightly vary something in your technique; maybe you try a different center of gravity or angle your legs slightly differently or move your arms differently. Rinse. Repeat. As the skateboarder tries different variations, the “learn” the intricacies of every move and slowly improve. The more time, the more variations and the more analysis they do, the faster they learn and get better. Over time, one can go from a novice to an expert, having built a massive repository of insights and training that help the brain leverage the learning to control the brain that in turn controls the muscles, bones and body weight to effortlessly skateboard. The AI Company’s platform is designed to mimic the process of learning to skateboard. However, instead of sequentially repeating the learning task, the AI platform enables automatically parallelizes the learning process by simultaneously trying out each possible variation for each move and then parallelizing learning multiple moves at the same time. This massive parallelization is accentuated by the automatic selection of the most optimal and accurate insights (AI models) that learn the best in the context of the problem at hand. The best AI models are automatically deployed to production, stored in a very secure form and can be leveraged in traditional app development or in the development of intelligent smart contracts (AutoLearn’s SmartChain). Imagine learning a skill instantly by parallelizing your learning so that you can try out the millions of variations, learn from them and ingest the learnings instantly. This is the AutoLearn boost.  With The AI Company’s AI, you are able to reduce what traditionally in data science would take upwards of a year and multiple data scientists to mere days through the automated training, selection and deployment of the best AI models out of 1000s of variations generated in parallel by AutoLearn. Not only do you reduce the time taken to go live with AI, because of the automation and the efficiency maximizer in AutoLearn’s AutoAI, you are guaranteed the best possible AI model. This is not guaranteed in a manually driven data science practice!
  • pintadachica on 09 Jan 19
     
    What is common between skateboarding and learning to skateboarding & autolearn.ai's AI platform. Lots, turns out. Consider the process of learning to skateboard. One repeatedly tries a move with the skateboard. You look at if you can land the move. If you do, you try a different move. if you don't, you slightly vary something in your technique; maybe you try a different center of gravity or angle your legs slightly differently or move your arms differently. Rinse. Repeat. As the skateboarder tries different variations, the "learn" the intricacies of every move and slowly improve. The more time, the more variations and the more analysis they do, the faster they learn and get better. Over time, one can go from a novice to an expert, having built a massive repository of insights and training that help the brain leverage the learning to control the brain that in turn controls the muscles, bones and body weight to effortlessly skateboard. The AI Company's platform is designed to mimic the process of learning to skateboard. However, instead of sequentially repeating the learning task, the AI platform enables automatically parallelizes the learning process by simultaneously trying out each possible variation for each move and then parallelizing learning multiple moves at the same time. This massive parallelization is accentuated by the automatic selection of the most optimal and accurate insights (AI models) that learn the best in the context of the problem at hand. The best AI models are automatically deployed to production, stored in a very secure form and can be leveraged in traditional app development or in the development of intelligent smart contracts (AutoLearn's SmartChain). Imagine learning a skill instantly by parallelizing your learning so that you can try out the millions of variations, learn from them and ingest the learnings instantly. This is the AutoLearn boost. With The AI Company's AI, you are able to reduce what traditionally in data
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5 signs why your digital transformation might be in trouble - The AI Company - 0 views

www.autolearn.ai/...sformation-might-be-in-trouble AI Company digital transformation learn cloud artificial intelligence
shared by pintadachica on 10 Nov 18 - No Cached
  • pintadachica on 10 Nov 18
     
    Digital Transformation is tough, even for seasoned technologists. This is because it is a transformation of an organization at its core. Everything from culture, technology, ideation, development, integration, delivery, and support needs to fundamentally shift to be more customer-centric, service driven, automation first and experimental in nature. No wonder that a lot of organizations take a long time and a lot of investment to see ROI from their digital transformations. Here are 5 signs that your digital transformation might be in trouble. Culture mistrusts the core digital transformation team You are spawning new initiatives before completing previous ones Decisions are top down with low accountability at the leaf nodes You tend to focus on technology stacks with little focus on customer value Inter-organization politics stifles cross-organization scenarios Culture mistrusts the core digital transformation team It is almost impossible to make an entire organization aware and participate in digital transformation at the same time. There are exceptions but in our experience, starting out with a core digital transformation team is a much better strategy than otherwise. This team should be enabled to attack a limited set of important and business relevant problems, build cutting-edge solutions and use them as examples to train and evangelize digital transformation strategies to the rest of the organization. However, the more entrenched an organization in the old way of doing things, the harder they might this central team. Resistance can be active and passive such as refusal to share data or provide the relevant context of the problem. An organization that does not set up the early crusaders for success almost always has a much harder time showing value from their digital transformation activities. You are spawning new initiatives before completing previous ones Executing on a digital transformation strategy is much harder than defining the strategy especially fo
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Beware of the integration! - The AI Company - 0 views

www.autolearn.ai/...-7-4-beware-of-the-integration autolearn artificial intelligence products technology AI company tech innovation integration software digital strategy
shared by pintadachica on 13 Dec 18 - No Cached
  • pintadachica on 13 Dec 18
     
    Enterprises have to constantly decide, at every step in their digital journey, should they build or buy. This question often is posed as a critical, do or die decision and the answer varies on a case by case basis. Building can be expensive, take longer but offers future proofing and more dependability whereas buying offers a faster time to market, less risk and accountability forced through contractual terms. However, a key point often overlooked is the cost of integration. Integration can be required at multiple levels. Vendor Applications Vendor applications typically require a two-way connection between the enterprise systems and the vendor application. The application requires incoming data and information from somewhere in the enterprise technology stack and an output stream of information back into the enterprise at one or more points in the stack or workflow. Vendor Platforms Vendor provided platforms typically have similar integration requirements as Vendor applications requiring an incoming data & information connection and an outgoing information connection into the enterprise process, workflow, platform or product. Application-To-Application Application to Application integrations where an application needs to be connected to another application to either provide data or signals to enable the downstream application to create value can be seemingly deceptive. Application-To-Application integration costs can grow at O(n^2) as potentially, worst case, each application could be connected with every other application. Enterprise Stack Fragmentation The problem of integration is exacerbated by the fragmentation of the enterprise at the organization level. This problem is also known as "Shadow IT" is driven by superficially differing needs of multiple lines of businesses in an enterprise. Shadow IT typically leads to multiple instances of similar technology stacks that cause data, compute and information to be silo'd. Stack fragmentation and its
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Is A Private Blockchain the way to go? - The AI Company - 0 views

www.autolearn.ai/...ivate-blockchain-the-way-to-go autolearn artificial intelligence innovation technology AI company software blockhain design
shared by pintadachica on 13 Dec 18 - No Cached
  • pintadachica on 13 Dec 18
     
    Blockchain are certain to disrupt almost all industries fundamentally. Though there are technical issues, the idea of utilizing a blockchain to prove ownership, to prevent double spending and to establish trust and transactions in an otherwise trust-deficient world, is gaining excitement and acceptance. More and more enterprises are getting curious about the blockchain and are willing to start investing in learning, prototyping, and building on top of the blockchain technology. Starting out with the blockchain requires investment in a building the domain expertise, establishing the identity, establishing the infrastructure and deciding the blockchain that the enterprise wants to work with. Among these decisions is the question: Should the enterprise choose a public blockchain or a private blockchain. Both have advantages and disadvantages and the question can often come down to what are the short term goals and potential applications that the enterprise wants to build on the blockchain. Public BlockChains The default option when considering blockchains is the public blockchain. This is the blockchain that is truly decentralized, leverageable for any type of transaction and in the case of the Ethereum blockchain, offers SmartContract authoring capability that makes the blockchain very attractive for building contracts that reflect the needs of the real world. However, for an enterprise deciding between public or private contracts, there are some considerations that require attention. Speed and Scalability Public blockchains tend to be slower as they are limited by the number of transactions that can be verified every second and confirmed every 10 minutes. There are several efforts underway to make blockchains faster and more scalable however those will take time to get implemented. Speed and scalability will continue to be an issue and contracts and applications that require instant or near real time execution will suffer from this lack of speed and scale. Se
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