Tips For Effective SEO And PPC Keyword Generation - 0 views
Tips For Effective PPC Advertising - 0 views
Can you transform into a tech company? - The AI Company - 0 views
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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
Skateboarding, The AI Company and the Autolearn Boost - The AI Company - 0 views
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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!
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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
Your Time Is So Important - Lush Dubai - 0 views
Time Management - 0 views
Get Low Cost Rent A Car - Lush Dubai - 0 views
Mobile Apps Are Driving A Resurgence In Advertising Creative - 0 views
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The 1950s and '60s were known as the brilliant time of advertising in light of the fact that no other period saw such inventive, weighty advertisement imaginative. Madison Avenue's "Psychos" were loved as well as were given the opportunity to analyze, the aftereffects of which empowered a significant number of their customers to rule piece of the pie. A promotion crusade's prosperity rose or fell on the correct jingle, strong joke or flawless mental trigger - all determined by an undaunted promise to magnifice........
Different Ways to Measure Your Marketing ROI - 0 views
Platform Commoditization: How not to get sidelined by commoditization - The AI Company - 0 views
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The Risk of Building Platforms: Cost of Marketing & Support
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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
Are You Leaving Money On The Table And Why A Monetization Strategy Is Key - The AI Company - 0 views
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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
Self Preservation: The Number One Hurdle To Innovation - The AI Company - 0 views
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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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