Technewsky: Artificial Intelligence

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    • Thursday, 1 November 2018

    Why AI Would Be Nothing without Big Data

    07:43:00 0
    Why AI Would Be Nothing without Big Data

    Artificial Intelligence is one of the latest concept of emergency technologies forces of our times. While there may be discussion whether AI will transform our international trend or evil ways, something we can all agree on is that AI would be nothing without big data.



    Influence future trend AI technologies have previous for several decades, It’s the explore of data the stuff of AI that has provided it to latest at incredible speeds. It’s the billions of searches done every day on Google that get a sizable real-time data set for Google to learn from our typos and search partially. Siri and Cortana would have only a basic understanding of our request without the billions of hours of spoken word now digital available that helped them learn of our send without the billions of hours of spoken word now digitally available that requested them teach our language. Match, Connie, the first attendant robot from latest banquet understands local language and receives to guests asking about the banquet, local client and more. The robot become smart intelligence due the crucial data it was given to learn now to procedure future input.

    AI continues to grow due to the ignition of data

    Every year, the number of data we generate doubles and it is feature that within the next decade there will be 150 billion chained sensors (more than 20 times the people on Earth). This data is active in helping AI gadgets learn how humans think and feel, and increases their learning sharp and also provides for the automation of data research. The more detail there is to process, the more data the system is given, the more it learns and eventually the more exact it becomes. Artificial Intelligence is now efficient of learning without mankind support. In just one example, Google’s deep learning freshly taught itself how to win 49 Atari games.



    In the past, AI’s growth was restrict due to limited data sets, typical samples of data rather than real-time, real-life data and the incapacity to research huge volume of data in seconds. Today, there’s real-time, always-available manage to the data and tools that enable quick analysis. This has moved AI and machine learning and provided the transition to a data-first approach. Our automation is now agile enough to access these giant datasets to quickly evolve AI and machine-learning applications. 

    AI enabled by big data

    Businesses in various industries are moving AI colonist such as Google and Amazon to implement AI solutions for their consulting. MetLife, one of the largest global providers of insurance, employee profit and amount programs, has also powered AI initiatives with big data. Speech identify has boosted the tracking of incidents and outcomes, the company has more well organized claims processing because claims models have been improved with unstructured data they now research such as doctor’s reports and they are working toward automated underwriting.


    Read more - How Big Data and Cloud Computing is Changing the Gaming Industry

    Will a computer ever be capable to idea like a human brain? Some tell never, while others say we’re already there. However, we’re at the point where the capacity for machines to see, understand and collaborate with the world is growing at a tremendous rate and is only increasing with the amount of data that provides them skill and understand even quicker. Big data is the bottleneck that ability AI.
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    Monday, 15 October 2018

    Why Successful Business Must Need Artificial Intelligence

    07:54:00 0
    Why Successful Business Must Need Artificial Intelligence

    Require agencies – those with long record – all battle war with same problem in the digital transaction: differ between transforming the business and working business needs. How does an organization its business structure midflight, while at the similar time aggressive operating settlement businesses in order to get security and cash flow? As the premier platform and network companies (HCL, IBM and Microsoft) boost, something the $1 trillion trend value recognize, the question becomes even more pressing.

    The discussion to this topic starts with aware the inter dependencies between brain, business and counting models about which we have existing written.



    A leader’s brain structure sets the calculate way for a company’s investments (capital allocation), values and count. Leader’s idea (their brain models), move into plan and then key highlighter enterprise.

    All enterprise cycles are a set of together requires and capabilities that include consumer actual needs, jobs skill, previous procedure and service providing and most critical capital allocation which mirror the leader’s mental model and measurement models.

    The inter connecting loop embracing, actions and calculate can either build a moral process of success, as in the case of recent platform hitting trillion dollar evaluations or set of unexpected outcomes, as is the case with experience CEO may have used.

    To build a virtual cycle experts quick start with their own way and the output actions. Put simply, if you and your core value products and services above platforms and networks than you will continue to create, market and sell more product and services. This not strategies plan. Previous product or service was the chief drivers of value for top-valued agencies (think refrigetor and zone and stuff and mesh). Today, however, products and services engage poorly with technology and networks. The manufacturers and seller and online retailers of things or services are down behind the value and boost of software and platform companies.



    Hybrid business structures. Increasing and augmenting settlement legacy products and services with latest and innovative technologies and platforms.

    Artificial intelligence (AI) is one of the mainly ways that agencies are moving legacy agencies into the modern age. AI can assist you better aware what is believing driving value in your agencies. This is great effort because it needs you to be exact output, while our expertise with dashboards and employees is that they are continue every day powerful, data need or without. To deploy this accessibility, companies’ wants to source data scientists who can guide gather and clean your organization valuable data ( from different community call list, legal document, consumer and employee remark report etc.) and use available AI tools from handlers like Google (TensorFlow), Salesforce and IBM. Once you get your dedicate team and stuff connect, it’s time startups the venture of real transformation using AI driven insights to power platform business models.

    More individual, use the precipitation from your AI-based to reverse your mental model. Put platforms and networks first on your panel meeting and with your team and therefore start to shift your business model and counting model. Alternative of goal on one-direction products and services, include your primary networks and include ways you can serve them by smoothing interactions and deal.

    How can I gather my previous networks so that they can advantages from shared feedback or review? Doing so will build a moral process of engagement that will in revolve build trust? Inspire everyone to co-build the future products and services that they and that you can create available to others on your digital platform;

    How can I include my existing networks so that they can advantages from shared experience?
    How can I connect data from all those communication and use AI to better explain the needs and requires of my network in step to build a better platform and boost the base of providers and consumers?



    Our expertise with hundreds of dashboards and management teams has educated us is that the best way to quick started is to optimize the board and senior handler on the various business model economies. Platforms and technology companies provided remarkable more benefit, boost and value compared to product and service companies. This isn’t easy venture provided that most panel team were educated and served most of their careers before the technology thunder. It’s solid for successful leaders to jump beyond what they know best, even if it’s insufficient business model.

    Dynamic structure, business and calculate model scaring to almost all CEOs, leaders and boards. However, defecting to hold AI, platforms and technology is worried with risk as large platform agencies like Walmart and Amazon gulp the boost, benefit and value in sector after industry. The only way to jump forward is to move your business to platforms and networks. Remember this isn’t an either. So debut deploying a hybrid today by collecting that secure your future success against the big other platform experts – Reputed brand and company – so you are not only a smash in the path on their venture of cross platform and value.
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    Thursday, 20 September 2018

    How to Combine Data Using Business Intelligence and Machine Learning

    03:55:00 0
    How to Combine Data Using Business Intelligence and Machine Learning

    As artificial intelligence (AI) and machine learning (ML) start to go out of academia into the business world, there’s been a number of goal on how they can achieve business intelligence (BI). There are a lot of unique in systems that use natural language find to help management more rapidly investigate corporate information, working analysis, and specify business action plan. A previous column discussing “self-service” business intelligence (BI) mainly highlighted two technologies where ML can help BI. While the user interface, the user experience (UX), matters, its visibility is only the tip of the iceberg. The data being provided to the UX is even more crucial.



    While that is useful, being able to reputation the information being displayed is even more critical. AI and machine learning can help address that issue.

    It Really Does Debut With Data

    While authority still exist, the day of the mainframe providing all data and detail is long gone. While the 1990s saw venture at data warehouses, information is a fluid platforms that exists in too many places to ever make the warehouse the “single version of truth” that some hoped. Today’s data lake is just the working data store on steroids. It will help but it will no more be a single repository than have the exits efforts at the same thing.


    Data survives in so many systems and the boost of IoT and cloud computing means data tracks enlarge far away from the standard of on-site computing. Working to analyze all the data and determine what is detail is a glowingly difficult issue.



    Therefore, the business has three key issue with the latest explosion in data:

    Without addressing those issues, the business is at risk through poor decision making based on inaccurate data and from increasingly strong data compliance regulations.

    Don’t Re-invent the Wheel
    Provided the issue, a solution is needed. Thankfully, there is no require to debut from scratch. Rather, there are techniques in other areas of software that can be held and accepted to the issue. ML ideas and other tools can be taken from other areas of IT to help both compliance and business decision making.

    Machine learning is creating inroads in network and application security. Best condition deep learning systems are investigating transactions to look for irregularity and identify attacks and other security risks. At same time, asset management systems are being pushed by both the explosion of mobile handsets and the growth of SaaS applications to better understand what physical and intellectual property assets are gathered to the joint networks and infrastructure.

    Those systems can be used to query network nodes finding for data sources in sequence to help develop an improved corporate metadata structure. Transactions on the community can be cross platform for new information and for specific usage.

    Helping Self-Service through Data Management

    Of critical useful, the ML system can help boost manage to data alongside accessing assent. It’s not enough in BI to search special case and identify threat. If analytics are honestly to become self-service, quicker access to information is necessary.

    In today’s structure, compliance guidelines and analyst power set an employee’s manage to databases and specific sector. That especially limitations self-service through the easy fact that we can’t imagine all requires ahead of time.



    As NLP gets an easy way for personnel to query business information, to understand business processes, and to discover new innovations between business data, there will automatically available concepts based in instinct and insight. An employee will ask a FAQ about data or relationships she hasn’t existing included, request data not yet manage, or otherwise fill-up to extend past the hard-set information boundaries.

    In the classic process, that means the analysis available to an unexpected stop, emails must be sent to IT, discussions must happen and then systems must be adjusted to allow new access rules.

    An ML system can individually speed that process, using guidelines and experience to rapidly search latest data, see if previous data fits within adjustment rules and allow immediate access, or flag the request for quick review by a compliance officer.


    This problem is more complicate than what is moving now with modifies in the UX, but the issue is just as critical. It doesn’t issue how simply a manager can ask a question if there isn’t a rapid move to understand where the detail to answer the question occupied and to decide if the questioner has the authority to know the answer.

    Machine learning gives a unique to far better update enterprise detail in today dispense world. While the industry move at ways to ask better questions, it requires to be finding at how to distribute and manage the information that provides answers.
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    Tuesday, 14 August 2018

    Why Your Company Needs AI

    07:55:00 0
    Why Your Company Needs AI

    AI's quickly flew in value-add has left agencies struggling to acquire this mostly complex technology. Chief scrap to understand it. At a basic level, the terminology is confusing, machine learning, deep learning, reinforcement learning, AI, etc... The business use cases are unclear, and the professionals are generally in academia, have their own startups or are at top tech companies.



    How businesses try to accept AI

    The accept process goes something like this: A decision marking reads or is told many times about how AI can do X for their business. The CTO or CIO looks into it and concludes that AI can probably help the company save costs. However, the benefits, AI behave and possible drawback may still be unclear.

    Next, the company might judge to hire a niche research experience. They're managed to develop an X system and are left alone to work it out. Finally, the outputs and assumption don't match, and the team is disbanded or re-focused on data science applications. Soon, the business pails AI in the "hype" category and jump on.

    I get it, getting an AI to work is hard - especially under business constraints.




    1. Your CIO is a skill in engineering - not AI

    Great CIOs aware how to optimize the software. They know the best ways of cutting prices or how to behave issues using the latest software engineering chart. However, she's unexpected to know about the new trends in AI and where they could help the company.

    To keep up with the new trends, AI analyst reads papers on a daily starting point, attend conferences, and host private research according from visiting scholars. Just in the past few years, the amount of latest research published in machine learning has grown quicker than any other sector. Although many papers are minor improvements, your companies needs a believer to sort out the critical developments and the suggestion for the business. Could be as easy as a latest workflow for identifying text that could immediately open up an entire new business line for your company.

     Read more - 4 Useful Future in Artificial Intelligence for Retail Sector

    The CAIO should be someone who is greatly knowledgeable about AI and familiar with latest approaches such as deep learning, reinforcement learning, graphical models, variation inference, etc. Without this skill, they might place approve reaches which are slow to implement, costly to manage, or that don't scale.

    2. A CAIO is your lifeline into the latest in academic research.

    It’s no confidence that the world's top AI researchers at big companies like Google and Facebook also hold academic links.

    Top IT agencies have found this method which provides them direct manage to the world's top AI qualify students. A strong academic link also provides for partnerships with these labs which can assistance gear hard business issues in exchange for the ability to publish outputs.



    To engage top AI talent, hire top AI researchers. To keep talent, your AI team must be offered to distribute to the open-source AI network and post displays. If you don't, they'll go to Google or Facebook where they'll have that freedom!

    3. The C-suite needs a branded professional who can build an AI to make new business lines

    Many companies not successful to fully leverage AI because the C-suite often doesn't explain AI capabilities. Hire a professional who understands the technology and understands how to resolve business issues with it. An AI expert in the room will abate covers about the income impact of a new AI system and unique business risks.

    I've seen worker with innovative ideas get shut down by the companies CEO because they don't understand the impact this latest system can have on the business. Don't let the c-suite lack of expertise in this sector track your organization from creating big AI-driven custom with vast unique upsides to your business. It's like not using the internet because you don't know how TCP sockets work.



    A top AI expert, argues that the Chief AI Officer is someone with the "business skill to take this new bright technology and contextualize it for your business." In need, someone with both the strong education niche and the business awareness to solve business issues using AI.

    4. A best CAIO includes feature to the C-suite

    Don't introduce your next product or business line without planning for a future how AI can help. AI's use in business is so new, that it's unexpected anyone in the explore suite is thinking about latest business lines that are now available because of AI. Show for issues that are complicated to scale, or that need a set of multiplex standard, these are main candidates for AI.



    Beyond the technical capabilities, your CAIO needs to have a good knowledge of the business. This is a person that knows when NOT to use AI. A good CAIO will create finally their team isn't searching for venues to fill AI, but instead fetching for issues that could profit from it.

    5. Data is an income river

    By now, organizations are actual that their data are widely valuable. If you trusts this predict, then it subscribers that you're mostly leaving a lot of money on the table by insert with classic methods that are known to perform other algorithms. A classifier that can piece users 20% more correctly means that you're mostly to put the correct products in front of that user. Why resolve for the machine learning charts that provide you 80% transparency when more new systems can get you to 90%?



    The CAIO joins the analytical and business skills wanted to supercharge your data monetization strategy.

    6. Highlight Wave

    If your business needs to gesture that you put AI thoughtfully, then hire a CAIO. AI is an afterthought in most companies. Don't create the same issue. Point will assist you attract top talent, rebrand your company's public see and highlight to investors that you're still innovating.

    7. Morality

    AI's use for various cases has come under inspection in few years. Now a day, a revolt within Google forwarded the company to promise not to develop AI secret weapons. The AI analysis, networking has created to voice purpose over ethics in the past year. As an output, top analysis are unexpected to produce AI to issues they deem unethical. The CAIO can help as the voice for an organization and drive the use of its AI towards profitable, yet ethical use cases.
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    Friday, 10 August 2018

    4 Useful Future in Artificial Intelligence for Retail Sector

    03:37:00 0
    4 Useful Future in Artificial Intelligence for Retail Sector
    In recently, I’ve been to some crucial event organized with attend by retailer sector, many brands and boost companies finding to grow traction in the retail ecosystem. Current year, I’ve detected continues boost in the number of AI-driven technologies and companies experiment solutions for retailers. I’ve also tested boosting like in these innovations among retailers? Because as gain chain for retailers without broken to find age-old issues such as incorrect listening, stock-outs and balance, overstretched and  untrained store associated and minimal pricing, latest in AI targeting these issues are improving their ROI.


    Artificial intelligence, machine learning, business intelligence, human intelligence or other intelligent behaviors believed by computers and machines that are “trained” by data to create independent decisions. AI offer to that we couldn’t past? It helps vast number of data, frequently from huge reference to test goal and solutions.


    This blog is the first of three that execute AI in retail. Here, I’ll cover the main use cases for AI in consumer-facing functions and share some i.e. of organizations that have developed AI application for retailers.

    Consumer Identify Facing Using AI

    At Coresight analysis, we’ve described the advance framework for retail AI use cases, which highlight to communication, optimization of efficient cost, build relationship and factual retail. The framework highlight illustrates how retailers can use AI to better busy consumers through transmission and experiences better support inventory and price products transparency.

    Advance Framework for AI in Retail

    Transmission

    Retailers are using AI to transmission with buyers through individual online experiences, informal automation and chatbots, and voice shopping. Its functionality in terms of customize is my target here.

    Popular e-commerce sites gather consumers on consumers’ own terms: they show buyers what they need to watch and get the information they want in order to make decisions and purchases. Mobile handsets, with their small screen sizes, make personalization even more remarkable. AI can help retailers create the best use of screen retail on mobile handsets, according highly relevant blog for each customer. It can acquire millions of customized home pages and email variant and personalize in-app experiences.

    One organization that individualize the e-commerce shopping experience by bringing the power of AI to behavior ecommerce standard or rule is Israel-based Personal. The company’s platform acquire the choice pricing gets and motivation for each shopper, based on the individual’s behavior layouts within the shopping session and in the past. Personally is paid only depend on profit influences, so the ROI is improved.


    How personal’s Incentive Personalization Platform Behave

    Optimization of Pricing


    Organization such as Amazon have huge amounts of pricing data and widely pricing applications that enable them to quickly respond to custom in competitors’ pricing and customer demand but long tern retailers haven’t classic had the similar tools. Now a days, AI debut and serves are easy the playing sector for old retailers by offering applications that modify prices not manually based on non-store data such as weather, local function and candidate advertising.

    Wise Athena, a revolutionary or innovator based Texas, guess SKU-level demand for CPG organization using machine learning, business intelligence and econometrics, fair improving the companies ROI on adverts. Wise Athena’s application optimizes data from competitors’ services entire retailers, locals and categories. It also tests how a company’s services interact with each other. The application and algorithms anticipate likely outcomes from pricing method with consider to cannibalization and service cross-elasticity, so CPG organization can collect the optimal promotion strategies to send to retailers.


    Example of the Insights Wise Athena Provides

    Justifications of Inventory

    AI-powered retail applications not only tested gap bud predict inventory and venue orders but also help low excess stock set-up, creating retail more efficient. Handle stock few ends up being checked down but AI application can help tested services that are given to be stocked in startup based on their ancient tendencies and block them from new startup.
    Startup gather data an analysis report suite that uses predict future and machine learning to survey retail inventories by offering models of future purchasing patterns and conduct. The company’s plan analysis application helps dealer aware how different products impact overall assortments. By testing underperforming products. Retailers can also watch which sector are being over allocated and those that have growth unique, so that they can re-setup mixture accordingly.
    A view of collect’s mixture test dashboard, which according influencing receipts at selected store.

    Experiential Retail

    AI has offline retail applications, too, and retailers can use it to aware offline activities with online vision. India-based Tailspin and US-based Peg are two companies that have deployed AI-powered mobile-/tablet-ready applications to help store connect get help and suggest to consumers. Other companies, such as Karros, use face identify and AI to identify consumers and aware store associates about their predication, as well as to calculate foot audience and demographic trends throughout the day, and even catch shoppers’ moods and regularity spans. Team can get this detail into account to deliver more personalized service, including displaying buyers with provides that are triggered by their past buy history.

    AI technology can assistance companies use all of these data to send better experience to their consumers and programmers continue to fetch AI application overall business functions. We think that the quicker retailers acceptable the technology, the greater the edge they’ll have versus their peeks.




    eCommerce site must need to test their individual strengths and drawbacks and include AI accordingly too busy with consumers effectively. The core framework can assistance online store pin down which of their online store method need quick attention.
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    Wednesday, 11 April 2018

    Why Artificial Intelligence Must Need Startup for a Business?

    08:21:00 0
    Why Artificial Intelligence Must Need Startup for a Business?

    AI may be providing the business network by storm, but crucial decision parson are still trying to   aware the revolutionizing new technology. Here are 5 main things every business decision marking want to know right now.

    Data available before AI:  Understand the difference between training and inference.

    Training an AI structure can be likened to a child learning a language.  Most children learn language from innumerable hours of attending to their parents and everyone else around them talk. They devour massive numbers of “data” over time and regularly learn the language.  Working on the time, the inputs, and the child’s attempt and aptitude, certain language proficients are achieved.

    Like a child, the AI model requires an objective. Then, it is built by uncovering the model to training data that reinforces the objective. With the right kind of data and volume of data, (likely “Big Data”), an enough amount of time and processing power, a definite level of proficiency (accuracy) can be achieved. Only then, like a child who learned how to speak and understand a particular language can they then utilize this knowledge to act and play.  Then the AI procedure can be used as segment of a business portability or process.



    Once trained, an AI model and the work pattern that it powers, can then be used in production, i.e. leveraged as part of a new or top product or service (i.e. think Alexa or Siri), a better consumer communication (think chatbots), or a latest or better way of doing business (think internet of things and predictive maintenance). This is called “inference”. There are numerous pre-trained models and systems that can be leveraged, acquired, or purchased. And if you can looking a model that significantly meets your requires, the secondary phase is already completed. So, if you buy a self-driving car, that car is always using “inference” to create driving decisions and take plans. But, someone else instruct that AI system to do that.

    The issue for a business is that their partitions may available from combining data from outside sources with data which is enjoy by the business itself. In include, the objective connected with the business value that the business can create may be various from that of a pre-trained system. There is active research around “transfer learning”, i.e. applying the training performed in one scenario to a various scenario. However, anyhow of whether move learning can be applied, a business is simply creating unique products and services, customer communications, and ways of doing business, and own unique data. So, “training” is wanted before “inference” and it all debut with data.

    AI is about trusted Relationships, not Functional Relationships, Executives want to comprehend Accuracy to generate business decisions around AI

    As the story goes, when Newton discussed an apple falling from a tree, he built a functional relationship between force, mass, and acceleration due to gravity. With this relationship, many things can be “resulted” from that method. AI does not run that way. In this portfolio, with AI, numbers or millions of different kind of things would be observed falling from different types of things and from different heights. Depend on “seeing” all that haven (being trained) the AI system could then make forecast (inference) about how other things will down. The AI system is numeric data. Individually, it will get some things right and some things wrong. The question that subscribe is how “accurate” is the AI system in making that forecast?

    In business systems, the more crucial question is how accurate does the AI system want to be? In the current state of AI, it seems, that with more and more data and calculating power, training and AI system can become more accurate. But, more and more data and computing power, and more functional skills to “wrangle” that data together at expensive price. So, a business expert needs to aware that not only does the objective need to be clearly tested, and the data needed to train to that objective identified, the mandatory accuracy needs to be defined.

    For quick a business creating a fun system depend on AI (i.e. identify dogs and cats) may not want to be that exact. However, a system used by consider to control a sign in a dog should be more accurate. A self-driving car that can calculate the difference between a dog and or some other spaces in the roadmap, requires to be extremely accurate. The accuracy wanted for an individual business system straight relates to data, training time, effort and computing power needed and so is a critical part of business cases.

    The calculation is not direct rocket science, the exact is. Business executives want to understand that AI success is more and more about the AI atmosphere used to innovate.

    As announced above, AI needs to learn (be trained). There is a lot of research going on around learning from smaller data sets. This is because successful AI today offers required accuracies by training on very large data-sets. And until this research bears fruit, successful AI training takes lots and lots of data and to train in reasonable time periods and takes significant compute resources.

    Today, AI bottlenecks trend to goal on absence of skilled “Data Scientists”, huge amount of students are choosing courses around Machine Learning (the data science part of AI) and even more are considering the MOOCs around AI like the not paid version for course fast.ai or Google’s free impact course on Machine Learning. There is also NVIDIA's Deep Learning Institute which offers online courses that provide you manage to a completely configured GPU-accelerated workstation in the cloud, complete with software tools, neural networks, and datasets.

    Finally, more key tools are coming out to make AI models, like Google’s TensorFlow and a lot of task is being put into AI platforms to automate much of the “data mugging” and “knob turning” that Data Scientists want to function. So, AI is becoming less about Rocket Science and more achievable and possible for more and more businesses. The clue that are not moving away right now are required for data and the want for framework to train on that data. So, business expert need to understand how they will have this Big Data/AI infrastructure or platform to grip so that AI innovation is realized within their business.




    The future is uncertain, but AI use case opportunities for business are understand today. Business chief need to understand the AI opening and issues individual to their business without being vague.

    There are a lot of creepy doomsday outline corresponding with talk of robots and Skynet around AI. This talk guides to Executives being distracted about the very interesting competition and awesome opportunities AI presents today.

    First, about the “scary” future. Today, the sizable AI Machine Learning “Deep Neural Nets” have tens of millions of sensory cell. The latest research on a human brain says we have about eighty six billion neurons with one hundred and fifty trillion connections. AI systems are just not there yet. In twenty or fifty years, that may modify.  A lot of analysis also think the current state of the way AI is taught will reach a brick wall. I.e. the research postulated that the way we are doing AI today is not the way the smart works. So not only are we at orders of magnitude less in scale and having challenging at that scale, the brain probably does things much various and better than the way we currently train AI networks.

    For business executives forwarding to improve consumer interactions, supply new method of doing business and delivering new products and services, AI is believed to be a must have. Today, there are real opportunities and problems for which business executives can leverage AI.  AI is getting better and better at computer vision (i.e. used for self-driving cars), natural language processing (i.e. for language translation or processing unstructured text), and speech recognition (i.e. think Siri or Alexa). Business want to innovate and AI provides that innovation opportunity that they or their competitors will leverage. They need to understand what they can detect, classify, segment, predict, or recommend that will allow them to create new ways of doing business, new customer interactions, and new or improved products and services.


    In addition, they need to understand the real risks about using AI today. That risk is centered on the quality of training data. Is that data faulty, biased, have errors or have other problems? A model develop with some data will then be mismatched, partisan, error prone, etc… Business Executives want to put training data very seriously. If they are leveraging pre-trained figures, how do they know what that model was trained on? If they are training AI, is the data they are using appropriate? It is one thing to amaze a dog or a cat because of bad training data, but an entirely various thing to treat a customer inappropriately because of improper data. Proper data access must be applied for all AI initiatives.

    If AI is to put Machine Learning and data to use for business benefit, Business Executives want to be directly and strongly involved.

    Right now, too often, whether it is because of time, lack of mastery, or not trusting that AI is consistent to their business, most executives are not strongly involved in AI genius within their business. They “outsource” this project to technical teams. Business Executives authority are perfectly around the success of their business. AI is not about expert research, it is about affecting data and machine learning to drive business success. Without business leadership, AI success in business will only be random and limited. Active and indeed proactive involvement of business leadership is critical. And that is why business executives want to understand the 5 things covered in this blog about AI right now.
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    Tuesday, 2 January 2018

    In 2018 - Big Data & Artificial Intelligence Impact of Sciences Sector

    03:03:00 0
    In 2018 - Big Data & Artificial Intelligence Impact of Sciences Sector
    Most of various IT industries with financial services have had a long track record of updating information and applying analytics to development consumer relationships and building new services, life sciences companies have only in current years start to fully embrace and grip upon the opportunities to manage and apply their data in a methodical way to a range of drug development and patient care problems

    As real science companies’ start to strongly mature their use of data, remarkable progress is starting made in the efficiency of software development and the standard of insights produced at the research stage. However, given the increasing size of learning about human resource and another processes, the opening to deploy data for even larger boost also continue to increase.



    Here are 5 major ways we see Big Data and AI impacting the Life Sciences in 2018:

    1. As both President Trump and Alex Azar, his applicant to execute the Department of Health and Human Services, have made clear, we can await the environment in the US to be progressively hostile to high drug prices.  It will, therefore, be require for life science companies to defend their research forecasts and their benefits margins by utilizing robust data that clearly demonstrate the value of their products.


    The opportunity to adequately coordinate data from the real world (e.g. medical insurance claims), genomic research and clinical behinds will offer real sciences companies to unlock answers to a host of standard  questions such as the true success of treatments which can then be used to defend pricing location in this increasingly tough market environment.

    2. Improving the speed and quality of bi-directional learning between the patient and the cure discovery procedure has been a basic strategy for life sciences companies in the last few years.  However, their ability to do this definitely has been hobble by poor data access and data quality issues.  As best practice in data strategy (including governance and architecture) advance to move through the industry we can expect the value unlocked by such translational medicine to accelerate.

    3. As risks and inability continue to dog many real science supply chains world, the employment of new technologies such as blockchain allows the likely to thoroughly advance levels of control and quality amount whilst at the same time reducing overall costs for infrastructure.

    4. As new branches of science deepen our knowledge of genomics and the broader implications of epigenomics, opportunities for utilizing AI to gain previously impenetrable insights are emerging over the horizon.  Although still very much at the research stage, indications are that these techniques will increasingly impact fields such as oncology.

    5. With all the different fields of study opening up beyond genomics and epigenetics (proteomics, metabolomics, transcriptomics, et al), it’s important to remember the wise words of Prof John Quackenbush of the Dana-Farber Cancer Institute, “At the end of the day, the most important ‘omics of them all is econ-omics.”  Accessing and analyzing the right data to deliver sustainable business value remains the central purpose for life sciences firms.

    We have known for a long time how a single drug can impact parts of the population in various ways.  With hospital Electronic Medical Records (EMRs) offering an progressively completing view of each patient, the ways in which, quiet issues notwithstanding, researchers can boost insights from this data into how their therapies are operating at a more granular level will be increasingly important to the conduct of life sciences companies as they fine tune the delivery and costing of medication to where it is most efficient for patient require and the corporate bottom line.  Opportunities for beneficial learning will accelerate further as EMRs and clinical trial technologies become increasingly integrated as we move beyond 2018.


    Whatever the coming year holds, one thing is beyond doubt:  Exciting new ways to create value and improve patient care await those firms willing to exploit the data tools and techniques that are now emerging.
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    Wednesday, 20 December 2017

    Complete Overview of Artificial Intelligence (AI)

    03:49:00 0
    Complete Overview of Artificial Intelligence (AI)
    The idea of artificialintelligence and the hopes and fears that are associated with its rise are fairly prevalent in our common subconscious. Whether we imagine Judgment Day at the hands of Skynet or egalitarian totalitarianism at the hands of V.I.K.I and her army of robots - the results are the same - the equivocal displacement of humans as the dominant life forms on the planet.

    Some might call it the fears of a techno-phobic mind, others a tame prophecy. And if the recent findings at the University of Reading (U.K.) are any indication, we may have already begun fulfilling said prophecy. In early June 2014 a historic achievement was supposedly achieved - the passing of the eternal Turing Test by a computer programme. Being hailed and derided the world over as being either the birth of artificial intelligence or a clever trickster-bot that only proved technical skill respectively, the programme known as Eugene Goostman may soon become a name embedded in history.

    The programme or Eugene (to his friends) was originally created in 2001 by Vladimir Veselov from Russia and Eugene Demchenko from Ukraine. Since then it has been developed to simulate the personality and conversational patterns of a 13 year old boy and was competing against four other programmes to come out victorious. The Turing Test was held at the world famous Royal Society in London and is considered the most comprehensively designed tests ever. The requirements for a computer programme to pass the Turing Test are simple yet difficult - the ability to convince a human being that the entity that they are conversing with is another human being at least 30 percent of the time.



    The result in London garnered Eugene a 33 percent success rating making it the first programme to pass the Turing Test. The test in itself was more challenging because it engaged 300 conversations, with 30 judges or human subjects, against 5 other computer programmes in simultaneous conversations between humans and machines, over five parallel tests. Across all the instances only Eugene was able to convince 33 percent of the human judges that it was a human boy. Built with algorithms that support "conversational logic" and open-ended topics, Eugene opened up a whole new reality of intelligent machines capable of fooling humans.

    With indications in the sector of artificial intelligence, cyber-crime, attitude and idea, it’s crushing to know that Eugene is only version 1.0 and its providers are already running on something more sophisticated and latest.

    Love in the Time of Social A.I.s


    Reference by - https://www.youtube.com/watch?v=5J5bDQHQR1g

    So, should humanity just begin wrapping up its affairs, ready to hand over ourselves to our emerging overlords? No. Not really. Despite the interesting results of the Turing Test, most scientists in the field of artificial intelligence aren't that impressed. The veracity and validity of the Test itself has long been discounted as we've discovered more and more about intelligence, consciousness and the trickery of computer programmes. In fact, the internet is already flooded with many of his unknown kin as a report by Encapsulate Research showed that nearly 62 percent of all web traffic is generated by automated computer programs commonly known as bots. 

    Some of these bots act as social hacking tools that engage humans on websites in chats pretending to be real people (mostly women oddly enough) and luring them to malicious websites. The fact that we are already battling a silent war for less pop-up chat alerts is perhaps a nascent indication of the war we may have to face - not deadly but definitely annoying. A very real threat from these pseudo artificial intelligence powered Chabot’s was found to be in a specific bot called "Text- Girlie". This flirtatious and engaging chat bot would use advanced social hacking techniques to trick humans to visit dangerous websites. 



    The TextGirlie proactively would scour publicly available social network data and contact people on their visibly shared mobile numbers. The Chabot would send them messages pretending to be a real girl and ask them to chat in a private online room. The fun, colorful and titillating conversation would quickly lead to invitations to visit webcam sites or dating websites by clicking on links - and that when the trouble would begin. This scam affected over 15 million people over a period of months before there was any clear awareness amongst users that it was a Chabot that fooled them all. 

    The highly likely delay was simply attributed to embarrassment at having been conned by a machine that slowed down the spread of this threat and just goes to show how easily human beings can be manipulated by seemingly intelligent machines.

    Intelligent life on our planet

    It’s easy to snigger at the misfortune of those who've fallen victims to programs like Text- Girlie and wonder if there is any intelligent life on Earth, if not other planets but the smugness is short lived. Since most people are already silently and unknowingly dependent on predictive and analytical software for many of their daily needs. These programmers are just an early evolutionary ancestor of the yet to be realized fully functional artificial intelligent systems and have become integral to our way of life. The use of predictive and analytical programmers is prevalent in major industries including food and retail, telecommunications, utility routing, traffic management, financial trading, inventory management, crime detection, weather monitoring and a host of other industries at various levels. Since these kind of developers are kept distinguished from artificial intelligence due to their profitable applications it’s simply not to notice their ephemeral nature. But let’s not kid ourselves - any interpretive program with manage to immense databases for the supposal of calling patterned behavior is the exact archetype on which "real" artificial intelligence programs can be and will be built.

    A significant case-in-point occurred amongst the tech-savvy community of Reddit users in early 2014. In the catacombs of Reddit forums dedicated to "dogecoin", a very popular user by the name of "wise_shibe" created some serious conflict in the community. The conference simply faithful to argument the global of dogecoins was lightly disturbed when "wise_shibe" grouped in the communications offering Oriental wisdom in the terms of clever remarks. The amusing and attractive conference offered by "wise_shibe" garnered him many fans, and provided the norms facilitation of dogecoin refund, many users made advance case systems to "wise_shibe" in replace for his/her "wisdom". However, soon after his rising popularity had earned him an impressive cache of digital currency it was discovered that "wise_shibe" had an odd sense of omniscient timing and a habit of repeating himself. Eventually it was revealed that "wise_shibe" was a bot programmed to draw from a database of proverbs and sayings and post messages on chat threads with related topics. Reddit was pissed.

    Luke, Join the Dark Side

    If machines programmed by humans are capable of learning, growing, imitating and convincing us of their humanity - then who's to argue that they aren't intelligent? The question then arises that what nature will these intelligences take on as they grow within society? Technologist and scientists have already laid much of the ground work in the form of supercomputers that are capable of deep-thinking. Tackling the problem of intelligence piece meal has already led to the creation of grandmaster-beating chess machines in the form of Watson and Deep Blue. However, when these titans of calculations are subjected to kindergarten level intelligence tests they fail miserably in factors of inferencing, intuition, instinct, common sense and applied knowledge.

    The ability to learn is still limited to their programming. In contrast to these static computational supercomputers more organically designed technologies such as the delightful insect robotics are more hopeful. These "brains in a body" type of computers are built to interact with their surroundings and learn from experience as any biological organism would. By incorporating the ability to interface with a physical reality these applied artificial intelligence are capable of defining their own sense of understanding to the world. Similar in design to insects or small animals, these machines are conscious of their own physicality and have the programming that allows them to relate to their environment in real-time creating a sense of "experience" and the ability to negotiate with reality.


    A far better testament of intelligence than checkmating a grandmaster. The largest pool of experiential data that any artificially created intelligent machine can easily access is in publicly available social media content. In this regard, Twitter has emerged a clear favorite with millions of distinct individuals and billions of lines of communications for a machine to process and infer. The Twitter-test of intelligence is perhaps more contemporarily relevant than the Turing Test where the very language of communication is not intelligently modern - since its greater than 140 characters. The Twitter world is an ecosystems where individuals communicate in blurbs of thoughts and redactions of reason, the modern form of discourse, and it is here that the cutting edge social bots find greatest acceptance as human beings. These so-called socialbots have been let loose on the Twitterverse by researches leading to very intriguing results.

    The ease with which these programmed bots are able to construct a believable personal profile - including aspects like picture and gender - has even fooled Twitter's bot detection systems over 70 percent of the times. The idea that we as a society so ingrained with digital communication and trusting of digital messages can be fooled, has lasting repercussions. Just within the Twitter verse, the future of using an army of socialbots to build suggestion topics, biased reviews, fake handler and the vision of unified diversity can show very dangerous. In huge numbers these socialbots can be used to border the public discourse on specific topics that are consider on the digital realm.



    This phenomenon is known as "astroturfing" - taking its name from the famous fake grass used in sporting events - where the illusion of "grass-root" interest in a topic created by socialbots is taken to be a genuine reflection of the opinions of the population. Wars have started with much less stimulus. Just imagine socialbot powered SMS messages in India threatening certain communities and you get the idea. But taking things one step further is the 2013 announcement by Facebook that seeks to combine the "deep thinking" and "deep learning" aspects of computers with Facebook's gigantic storehouse of over a billion individual's personal data.

    In effect looking beyond the "fooling" the humans approach and diving deep into "mimicking" the humans but in a prophetic kind of way - where a program might potentially even "understand" humans. The schedule being created by Facebook is amusing called DeepFace and is presently being touted for its revolutionary facial recognition technology. But its broader goal is to survey existing user accounts on the community to suggestion the user's future plan.

    By incorporating pattern recognition, user profile analysis, location services and other personal variables, DeepFace is intended to identify and assess the emotional, psychological and physical states of the users. By incorporating the ability to bridge the gap between quantified data and its personal implication, DeepFace could very well be considered a machine that is capable of empathy. But for now it'll probably just be used to spam users with more targeted ads.

    From Syntax to Sentience

    Artificial intelligence in all its current form is primitive at best. Simply a tool that can be controlled, directed and modified to do the bidding of its human controller. This inherent servitude is the exact opposite of the nature of intelligence, which in normal circumstances is curious, exploratory and downright contrarian. Manmade AI of the early 21st century will forever be associated with this paradox and the term "artificial intelligence" will be nothing more than an oxymoron that we used to hide our own ineptitude. The future of artificial intelligence can't be realized as a product of our technological need nor as the result of creation by us as a benevolent species.


    We as humans struggle to comprehend the reasons behind our own sentience, more often than not turning to the metaphysical for answers, we can't really expect sentience to be created at the hands of humanity. Computers of the future are surely to be exponentially faster than today, and it is reasonable to assume that the algorithms that determine their behavior will also latest to inculcate highness, but what can't be known is when, and if ever, will artificial intelligence reach sentience.



    Just as difficult proteins and intelligent life search its starts in the early pools of raw materials on Earth, artificial intelligence may too one day appear out of the complex interconnected systems of networks that we have created. The spark that justified chaotic proteins into harmonious DNA strands is perhaps the only thing that can possible evolve scattered silicon processors into a vibrant mind. A true artificial intelligence.
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