Technewsky: Machine Learning

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Monday, 29 October 2018

What is the future of R and Machine Learning – How to Use Career Perspective

04:02:00 0
What is the future of R and Machine Learning – How to Use Career Perspective

Why Machine Learning Need?

If mention to an application in the field of latest technologies like AI (Artificial intelligence) that send the systems the aptitude do learn automatically and enrich the experience without being straight coded which means learning to be automated rather than being coded exact.

It is apprehensive with developing the computer programs that have the expert set to manage the data and then using the data for the motive of learning.



The entire process startup begins with the absolute number of data or the analysis creating the examples into reflection. Offering the examples or individual instructions the computer programs help in testing and research the layouts in the data. This would further help companies or the business agencies in taking better decision based on the examples that we gets to the system which differ as per the conditions or business issue.

The inclusion of experience into its tasks would eventually enhance the learning of the systems. The prime goal of the machine learning is to make the systems automated so that human intervention or interference is not required.

Why R Programming Need?

R, a programming language, is the best career option considered for it since this language is utilized in statistical or data analysis.

All the techniques needed in the field of data analysis, such as future modeling, sampling, visualization etc. are provided in R. It is powerful and is the most popular tool in the field of machine learning.

This language helps in offering the analyzed and researched data to the automated systems developed which means the investigation and explanation of the data are done by R and it also assists in evaluating the end results of the learning algorithm.

How to Inter Connect Machine Learning& R Programming

If you are an enthusiast for machine learning, then it becomes essential for you to have a detailed knowledge of the programming languages and for that R is considered to be the best one if you are more into the statistics and the mathematical perspective of the machine learning.



In this field, R assists you in developing and deploying the machine learning patterns, while dealing with the data-sets and their rapid prototyping.

The R programming further helps in evaluating the machine learning algorithms and assists in learning the steps required for investigating and cleaning the data which means getting the hands dirty with data.

This learning with R makes you eligible for various job profiles in the field of analytics and technology. Some of the career options available are as under:

1. Data Scientists: The role of the data scientist relates to working on mathematics and using the existing methodologies to derive the inbuilt patterns and the useful insights from the data flowing into the organizations.

2. Machine Learning Engineers: Their role is concerned with building the applications and the programs using tools or techniques.

3. Researchers: Their role is concerned with building new techniques and tools that can enhance the ability of systems to learn more effectively and efficiently.


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.


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.





Wednesday, 17 January 2018

Machine Learning Crucial Role for Successful Business

03:40:00 0
Machine Learning Crucial Role for Successful Business
Machine learning (ML) algorithms offers computers to specify and distribute rules which were not exactly explicitly by the developer.

There are entirely a lot of articles dedicated to machine learning algorithms. Here is an attempt to create a "helicopter view" description of how these algorithms are provided in various business areas. This list is not an exhaustive list of course.

The first point is that ML algorithms can help people by helping them to search patterns or dependencies, which are not visible by a human.



Numeric forecasting seems to be the most well-known area here. For a long time computers were seriously used for predicting the behavior of financial markets. Most structure were developed before the 1980s, when financial markets got approach to sufficient computational power. Later these technologies explore to other industries. Since computing power is cheap now, it can be used by even small companies for all kinds of predications, such as traffic (people, cars, and users), sales forecasting and more.

Anomaly disclosure algorithms help people scan lots of data and tested which cases should be checked as anomalies. In finance they can analyze fraudulent transactions. In infrastructure auditing they make it possible to identify problems before them impact business. It is used in manufacturing standard control.

The main idea here is that you should not detail each type of anomaly. You give a big list of various known cases (a learning set) to the system and system use it for anomaly identifying.

Object clustering algorithms offers to group big amount of data using vast range of meaningful specification. A man can't operate efficiently with more than few hundreds of object with many parameters. Machine can do clustering more profitable, for example, for customers / leads qualification, product lists segmentation, consumer support cases classification etc.

Suggestion / resources / behavior forecasting algorithms gives us opportunity to be more efficient interacting with consumers or users by helping them exactly what they need, even if they have not thought about it before. Suggestion systems works really bad in most of services now, but this industry will be improved quickly very soon.



Resource for - https://www.coursera.org/learn/machine-learning/lecture/Ujm7v/what-is-machine-learning

The second point is that machine learning algorithms can replace people. System makes analysis of people's actions, build rules basing on this information (i.e. learn from people) and apply this rules acting instead of people.

First of all this is about all kind of good decisions making batter opportunity. There are a lot of operations which want for standard actions in standard conditions. People make some "standard decisions" and escalate cases which are not standard. There are no reasons, why machines can't do that: documents processing, cold calls, and bookkeeping, first line customer support etc.

And again, the main feature here is that ML does not want for explicit rules definition. It "learns" from cases, which are already resolved by people during their work, and it creates the learning procedure cheaper. Such systems will save a lot of money for business owners, but many people will lose their job.

Another fruitful area is all types of data harvesting / web scraping. Google aware a lot. But when you need to get some collecting structured information from the web, you still require to attract a human to do that (and there is a big chance that result will not be really good). Information aggregation, structuring and cross-validation, based on your preferences and requirements, will be automated thanks to ML. Qualitative analysis of information will still be made by people.

Finally, all this approaches can be used in almost any industry. We should take it into account, when predict the future of some markets and of our society in general.


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.



Friday, 15 December 2017

5 Ways Organization May Want to Implement AI

03:22:00 0
5 Ways Organization May Want to Implement AI
Artificial Intelligence is an isolating subject, with both advocates and skeptics dominating the headlines. Elon Musk and Stephen Hawking have notified about AI’s environmentally possible, while others have more grounded covers affecting to automation and jobs. Hysteria and hyperbole move to surround anything new, but CEOs and organizational chief covering industries have the chance to take a levelheaded approach to AI and it’s unique.

The modern technology is somewhat growing. Although the eventual remains hazy, investing in education is the smart play. Most of firm want to better knowledge about AI's potential if their leaders hope to stay ahead of the game. Here are five ways you can apply a similarly measured approach and ensure your organization is well-positioned for the future.

1. Invest in AI-related research and innovation.



The Artificial Intelligence sector has grown by leaps and bounds in current years, but its real tangibility rest of unclear. Still, this shouldn't avert companies from forging ahead and fetching appropriate uses for AI. Indeed, obtaining a first-mover benefit can be worth the cost of investing in analysis and buildup.

Following to the McKinsey Global Institute, technology mainstays such as Google and Baidu invested between $20 and $30 billion in AI during 2016, with 90 percent of those figures channeled stright into R&D. Startups also saw the signs, allocating $6 to $9 billion to AI research. More crucial, at least 20 percent of AI-aware companies reported themselves as early adopters.

AI has made a specific variations in several clear-cut use cases. Motorcycle manufacturer Harley Davidson more changing to lead generation by 2,930% in the three months after it implemented an AI-based marketing system named Albert. Other companies are showing powerful outputs for AI, IoT and machine learning -- particularly when it available to creating actionable business insights and growing sales.

Approximately 80 percent of companies including AI solutions have exploit from better insights and analysis, following to Capgemini's State of AI research for future trend. AI also enabled JP Morgan’s legal supporter, which probably spends hundreds of thousands of hours considering offers, to analyze thousands of data in seconds while immortally decrease errors.

Research's target is finding applicable-use cases, then changing AI technology to provide the company's requirement. Implementing AI for adoption's sake never should be the standard. That route hardly leads to true operational upgrading. 

2. Use AI as intended: to complement, not replace.



One of the largest fears surrounding AI is that the technology will cheapen the value of human capital. The argument attend this logic: AI leads to automation and lower the want for costly human labor because machines can work the same functions with higher productivity and less outgoing.

While gripping, this quarrel is somewhat flawed. The same Capgemini learning found the bulk of companies observed saw increases in job opportunities beside improved efficiency and service. Actual aware how AI can complement a company’s workforce is far more creativity than worrying about how it will lost the labor force.

Also, most AI technologies still are limited specific to human capability in few sector. For best outputs, companies should generate systems that highlight the energy of each. KLM, for example, achieved an AI-assisted consumer-service model. The system uses AI to interpret analysis through the company’s delivery channels and offer probable responses to agents, cutting down wait times and developing passengers' totally satisfaction.

Others, such as China Merchants Bank, have replaced front-line support with AI-enabled Chabot’s that can resolve most basic queries. This gives support staff greater time to focus on customers with bigger, more complex problems. The lesson learned? Adapt AI technologies to fit areas of real need and find sectors in which technology can help people do their jobs better. In the process, companies effectively will streamline operations.

3. Educate yourself and your team.



Revolutions and modern technology always available with an understanding gap. While early accept are learning, the dominant tends to be a lot steps behind. A joint survey from BCG and MIT’s Sloan Management Review found that officers in most industries consider AI technology will have an important overview during the next few years. Companies are starting to recognize the likely for AI-based platforms and the ever-increasing want to be informed. BCG and Sloan noticed that 83 percent of defendant viewed AI as a critical opportunity for expansion.

For most, expanding a high-level technical knowledge of AI is not mostly requirement. What is critical, though, is understanding the technology enough to admit its potential indications. Chief should be aware of AI requires, such as how projects understanding from detail, how AI systems can be included into every-day tasks and how investing in evolutions could better position firms for future competition.

At the same time, leaders constantly should analyze their workforce to searches areas in which AI application could improve operations and offer tangible boosts. Employees should be instruct and educated in AI via online courses, manage and similar programs developed to help them prepare for the impending proliferation of AI technology.

4. Create new jobs to manage AI fields.


Some call that experts and other highly technical professions will be highlights hardest by the emerging AI boom. However, engineer’s studies and industry trends recommended otherwise. While technological innovations may cause job losses in the debuting, these benefits offer a long-term balance: new jobs and other sector to handle the work. 

To endure admissible, AI-aware companies should debut shifting jobs and available convenience toward a paradigm that accompaniment the new technological direction. AI can replace many lower-level tasks required in day-to-day operations -- data optimize and marketing among them -- but these systems still will require analysis and constant acceptable.

Its amazing way to develop these jobs most of companies and not solely in technology-related departments. System maintenance is decisive, but understanding the application of AI systems is a frame question. It wants several sectors to identify a relevant use case and ensure easy adoption.

5. Keep the 'human' in HR.



AI is evolving and regularly fetching new uses, and experts always should endeavor for a sense of balance in how these planning are completed. Automation might seem encouraging in all cases, but some sectors want a less analytical, human touch. General Human Resources, for example, requires analytical expertise but also the capacity to be intensely available when answering to employees' needs and concerns.

Though machines can handle one aspect of the HR job description, employees feel more comfortable and heard when another human is current. AI will remain valuable in areas such as payroll, recruitment, understanding employee pre-planning and providing labor. But AI never will fully replace your HR department. While focusing on analytics alone will result in unhappier employees, forward-thinking companies already are using AI to give their HR staff members tools to do their jobs better. 

Even the confusion surrounding the sector, one thing is for often: AI will keep expanding. Successful case studies and breakthroughs showcase the technology’s possible and sustainability. In the face of this new wave, companies are well-advised to continue creating, discovering and revolutionary to become forerunners in AI applications.


Friday, 13 October 2017

Deep Learning with Machine Learning & Artificial Intelligence Systems

23:02:00 0
Deep Learning with Machine Learning & Artificial Intelligence Systems
It seems as though everywhere we look, and in just about everything we read humans use metaphors to describe things. It happens when you are talking to your friends, and it happens when you are reading nonfiction or fiction, we see it on billboards, in advertising, in the newspaper, and we hear descriptions using metaphors in the TV news. Even teachers use it when educating our children, metaphors are truly everywhere. In fact, I would challenge you to go one day of your life without using a metaphor. I bet you it would be harder than you think.

There was an interesting article recently in Network World's Online News and Blogging Network by Michael Cooley on his Layer 8 Blog titled "Apple of My Eye? U.S. Fancies a Huge Metaphor Repository - The US Intelligence Advanced Research Projects Agency (IARPA) Building a System to Understand Human Metaphorical Speech".



"IARPA wants a repository of American/English & for Iranian Farsi, Mexican Spanish & Russian. It has been known since Aristotle that rhetorical devices are unique, creative instances of language artistry. They shape how people think about complex topics & influence beliefs; reduce complexity by expressing patterns; & show uncovered inferred meanings and worldviews of particular groups or individuals."

As a writer, this concerns me very much, because as soon as artificial intelligent computers can understand human language, and the difference between a metaphor, a story, versus actual nonfiction, then it will also be able to create stories, create new metaphors, and therefore someone like me who likes to write creative fiction as a hobby, or even science fiction, well, I simply won't be needed anymore.

I will be replaced by a computer.

Now then, you might say that it is impossible, that it will never happen, but I guarantee you it will, and that day is coming. Robotics, computers, artificial intelligence, will eventually replace almost nearly every human job, even the creative ones. Now then, some people believe that artificial intelligence will never reach the level of human creativity. This is because it is very hard to get a computer to become creative, or at least that's what they say. Personally, I considered several ways to get AI software to simulate human creativity.

After all, there is that philosophical thought that there are no original thoughts, that humans simply take one thing and re-assimilate the information borrowing observations and experiences from one area of the human endeavor and combining it with others creating a third. Then they call it creative, but is that real creativity, or is that just a recombination. Because if it is a recombination and that's all that human creativity really is, then surely we can design AI software to do that. Indeed I hope you will please consider all this and think on it.


Tuesday, 12 September 2017

Digital Technology Trends That Will Dominate in Future

03:49:00 0
Digital Technology Trends That Will Dominate in Future
Personally, I’m surprised at the technology we have available to us. It’s amazing to have the ability to recover almost any information and communicate in a thousand various method using a handset that suites in your pocket.




There’s always thing new on the scope, and we can’t help but wait and amazement what technological marvels are coming next.



The path I show it, there are some factor to tech trends we’re in store for in future. If you’re eyeing an industry in which to debut a business, any of these is an attractive good bet. If you're already an entrepreneur, think about how you can leverage these technologies to reach your target audience in new path.

1. IoT and Intelligent Home System




We’ve been ranging about the future innovation of the Internet-of-Things (IoT) and output association of smart home technology for years. So what’s the holdup? Why aren’t we all income in smart, connected homes by now? Part of the issue is too much competition, with not enough collaboration—there are tons of specific machines and apps on the trend, but few solutions to chain everything together into a single, seamless user experience. Now that bigger companies already well-versed in uniform user experiences (like Google, Amazon, and Apple) are getting involved, I predict we’ll see some major promotion on this front in the coming year.


2. AR and VR Technology




We’ve already seen some big step going for augmented reality (AR) and virtual reality (VR) technology in 2017. Oculus Rift was released, to approving acceptance and thousands of VR apps and games followed. We also saw Pokémon Go, an AR game, explode with over 100 million downloads. The market trend is ready for AR and VR, and we’ve already got some early-stage hardware’s and tech for these applications, but it’s going to be next year before we watch fact things take off. Once they do, you’ll want to be ready for AR and VR versions of nearly everything—and ample marketing strategy to follow.


3. Machine Learning




Machine learning has taken some huge treads ahead in the past few years, even appearing to guide and increase Google’s core search engine algorithm. But again, we’ve only seen it in a limited scope of applications. Innovative Throughout in Future, I predict to see machine learning updates emerge across the board, spearing almost any kind of client application you can think of, from providing better suggested products based on prior acquire history to slowly boosting the user experience of an analytics app. It won’t be long before machine learning becomes a type of “new normal,” with people awaiting this kind of AI (artificial intelligence) as an element of every form of technology.

4. Automation Job



Vendors will be (mostly) content to learn that automation will become a bigger pillar in and throughout in Future, with modern technology allowing the automation of previously human-exclusive functions. We’ve had electronic reporters in movement for a pair of years now, and I predict it won’t be long before they create another jump into more applied kinds of blogs. It’s likely that we’ll start watching efficiency skyrocket in a number of white-collar kind jobs—and we’ll debut seeing some jobs disappear altogether. When automation is combined with machine learning, everything can improve even faster, so 2017 has the potential to be a truly landmark year.

5. Big Data in Future



Big data has been a suggestion topic for the past few years or so, when it commerce creating headlines as a buzzword. The great think is that stack amount of collect data—which we now have approach to—can help us in everything from designing better medical treatments to perform better creating campaigns. But big data’s greatest force—it’s perceptible, numerical foundation—is also a defect. In past few years, I await we’ll watch promotions to improve big data, working more empathetic and qualitative bits of data and projecting it in a more visualized, accessible way.

6. Physical-Digital Integrations



Mobile devices have been steadily attaching technology into our daily lives. It’s rare to see anyone without a smartphone at any given time, donating us entry to virtually extensive information in the real-world. We already have things like site-to-store buying, enabling online consumers to purchase and collect products in a physical retail location, but the next level will be even further integrations between physical and digital realities. Online brands like Amazon will debut having more physical products, like Dash Buttons, and physical brands like Walmart will debut having more digital atmosphere, like store maps and product trials.

7. More Demandable



Many people are getting used to having everything on demand via online smartphone apps. In future, I predict this to show this develop even more. We have number of apps available to us to get more food deliver and even a venue to stop for the night, but soon we’ll watch this extend into even new corner region.


Anyone in the tech sector knows that creating projections about the route of technology’s future, even a year out, is an effort in failure. Surprises can available from a digit of various directions, and declared developments hardly leave as they’re intended.

Still, it pays to forecast what’s coming next so you can construct your marketing method (or your budget) accordingly. Whatever the case may be, it’s still fun to think about everything that’s coming next year.