5 Ways the IoT Can Change the Business World

5 Ways the IoT Can Change the Business World

Internet of Things has started transforming businesses in 2020

The use of cutting edge technologies like AI,IoT and 5G in the business world is no more different.IoT devices going to work in close connection framework to each other and will be controlled to improve efficiency, which in turn has direct impact on the productivity of the business. More work can be done in less time.IoT devices record and transfer data to monitor important processes, give us new insights, boost efficiency, and allow companies to make more informed decisions.The increment in productivity and efficiency will increase your profits significantly.

Here are possibilities of the near future Internet of Things (IoT). This information is based on a survey of 3000 executives in 12 countries and on the company’s own IoT expertise, as well as feedback from customers and partners.

– The Internet of Things has gone from being a buzzword to becoming a part of everyday life any future-oriented business must relate to. It’s too late to ask if IoT, or the Internet of Things, has value. In 2019, the question must be how we can make the most of it. This is exactly why we enlisted the IOT 2020 business impact.

This is what the energy specialist believes are the five most important possibilities of the future IoT:

5 Ways Today’s IoT Will Affect Your Business

1.Mobile Employees

With tomorrow’s IoT, we get a new digital wave that just connects things (Things) using the Internet. This makes us more mobile and more digital than ever. The digital wave is accelerating interconnected sensors at lower prices, artificial intelligence devices, faster networks, cloud services and increased capacity for advanced data analysis. With the new wave, the farmer does not have to be in the barn to check if the cow is satisfied.

2.Customer Satisfaction and Loyalty

IoT enables us to take advantage of unused data sources to enhance the customer experience. Although many companies are thinking about efficiency and lower costs when considering the value of IoT, access to huge amounts of data and the ability to retrieve real-time information is perhaps most important. IoT can provide even better customer service and new opportunities for customer satisfaction and loyalty. Who would not like a customer center that has the solution ready the moment we get through the telephone queue?

3. Combines Security and Flexibility

An open, compatible and hybrid way of working is the basis for tomorrow’s IoT. It requires collaboration on global cyber security standards. In addition, cloud-based IoT will grow, both in popularity and in diversity, across systems. When IoT solutions become available to most people, the solutions will be tailored to both security needs and tasks to be solved. With a little luck we can adapt the safety to the job and not the other way around.

4. New Sources of Revenue and New Business Models

Just as innovation and development have been driven by the industrial revolution, the mobile phone and the internet, IoT will lead to new ways of making money and new business models. Schneider Electric’s Energy Operations software and Building Analytics are two good examples of IoT used in property management. Thanks to grid-connected sensors combined with analysis software and assistance from the company’s energy advisers, operators can now be immediately notified of system failures and intervene immediately before causing downtime or energy loss. The data stream from the plants is aggregated into monthly reports that point out specific efficiency measures. The system has been used by NTNU, among others, at Campus Gløshaugen in Trondheim with excellent results.

Companies, cities and especially developing countries will benefit from IoT solutions, as these solutions are freer and do not have to comply with traditional laws and regulations. According to the consulting firm McKinsey, today’s developing countries account for as much as 40 percent of the market for IoT solutions.

5. Helps the Environment

IoT solutions help to address some of our greatest challenges, namely global warming and pollution. In fact, Schneider Electric’s report shows that expectations for IoT are highest when it comes to the effect on climate and the environment. Both public and private sectors are using IoT solutions in the fight against global warming. For example, the University Hospital in Northern Norway has automated Europe’s largest patient hotel and delivers world-class energy efficiency. Read more about it here .

Still unused potential

Even now in 2019, IoT is delivering great value results. Still, according to the energy company, there is great potential for more. According to Schneider Electric, IoT solutions are the most useful in these four areas:

1. Maximize energy efficiency and sustainability through smarter systems and faster decision making. Gigantic Excel sheets are now being replaced by real-time mobile control.

2. Optimized machine and system use as a result of good monitoring and analysis. For example, with critical material and temperature measurement sensors, you can find weaknesses and avoid downtime.

3. Smart, productive and profitable operation through cuts in use of time and resources. Real-time analytics allow you to customize your operations or production as needed from time to time.

4. Mobile monitoring and reduced risk due to simulation and digitalisation. With today’s IoT solutions, you can check the factory’s machinery on the cellphone on the couch and be on the alert when the accident happens.

What risks do IoT security issues pose to businesses ?

The threats of the future will be more targeted and that new technology will make it easier.
Must increase focus on IT security among SMEs


As the security of large companies has improved, the attacks will increasingly come against smaller companies. It is not only useful to secure the big ones – everyone must be safer, if the country is to become safer, Dficlub concludes.

More targeted attacks

We believes the threats of the future will be more targeted and that new technology will make it easier to attack smaller companies.


Everyone is finding the starting point for the digital transformation and the road to mature production in the IoT age. Terms like Smart Manufacturing, Industry 4.0, Digital Transformation and Industrial Internet of Things (IIoT) are no more getting more practical and applications based: We are bombarded by new concepts and hyper that remind us daily of the rapidly changing world of manufacturers.

Everyone can find an excuse to ignore them, but you can be sure this is not a hype. The world is changing faster than ever, and manufacturers can’t afford to be left behind.

Research based firms has studied the industrial software market for several years, and researchers interviewed hundreds of industrial players. There is a common theme that goes back; – To get started, one must find the starting point for the digital transformation. Results shows how production management systems (MOMs) can be a low-risk and effective starting point for companies that see production as a key part of the digital transformation.

MOM; The concept shows how production and operations management can use business organization and management concepts in the production of goods and services. But a big challenge that IoT based industries are going to face is security.

– If you look at blackmail via email, this is a sign that the attacks are being targeted. The attacks are based on the company’s internal resources and can, for example, attach previously used usernames and passwords to make the attack feel more personal.

Bjarte Malmedal was in the first person that received a master’s degree in information security at the Gjøvik University College. After his studies, he worked for over 20 years with security management and operational cyber security in the Armed Forces, and was instrumental in establishing and leading the defense cybersecurity center. The environment was one of the first to work systematically with operational information security in Norway, says Malmedal, who currently works as chief consultant for Experis Cybersecurity.

Security culture and the human factor

– It was a major cultural shift to move from the defense’s operational and technical security efforts to focus on the human factor. The project at NorSIS was to provide a systematic overview of what has been done in the field of security culture in Norway, and was one of the first of its kind in the world. It was therefore a highlight to present the report to the International Telecommunications Union (ITU), a UN body for global standardization in telecommunications, he says.

Malmedal was also responsible for reports on safety culture in the energy and water supply sectors, as well as for the youth segment. The project has now collected data for over four years and provides the basis for analyzing trends and changes over time.

Must secure the entire value chain

He says that many are concerned about security culture, but lack a clear picture of what it actually means.

– In case of data breaches, companies often blame poor security culture, but when you ask what it actually is, they get an answer. One must therefore break the term down to attitudes, knowledge, or behavior. The report is a guide to identify how the situation is in your own organization.

As editor of the government’s new national IT security strategy, which was presented in January, he looked at how the situation is in business, and especially for small and medium-sized businesses.

– Previously, SMEs were not considered important in terms of security. The idea was that something much worse is happening with, for example, Hydro, but the development of the digital economy has led the major players to depend on the security of the subcontractors. If you look at the latest major security incidents, the attacks often come via subcontractors, and today it is understood that the security of the major players is entirely dependent on the security of the small players, he says.

Must lift the small businesses

He points to a comprehensive lack of IT security expertise among small and medium-sized businesses.

– Smaller companies do not have the money to hire their own people to work with security, but must focus on the core business. If a small business goes to the major security players, they are often offered comprehensive and costly solutions. It is difficult for small businesses to find good security solutions, and we therefore want to help small businesses reach a reasonable level of security.

He refers to a survey conducted by Experis this fall, in which 300 business executives in SMEs were asked about IT security.

– Nine out of ten business executives consider IT security an important one, but only half responded that they had sufficient expertise internally. The general attitude is that IT security is important, but the ability to do something about it is not present.

5 Reasons Why 5G is The Future

5 Reasons Why 5G is The Future

5 Reasons to Look Forward to 5G

The next generation mobile network is more than “just” crazy speeds …

We are approaching a society where everything and everyone is connected through the internet – at tremendous speeds, thanks to the 5G network.

Many mobile operators recently opened 5G pilots in limited areas , and are thus well on track before next year’s large-scale rollout of the super network of the future.

And there are several reasons to rejoice. Here are five of them …

1. Huge speeds
That being said, first of all: 5G will offer incredible speeds.

– Year after year, data usage is growing by between 50 and 100 percent. 5G will enable much more data to be transported than today’s 4G network, and is therefore far more mature to cope with our increasingly advanced data usage.

According to recent 5G test results , we will notice this especially when streaming or using entertainment services:

– Playing on 5G will go like a dream, with speeds at the level of fiber.

The fact that there is hardly any latency to talk about in the 5G network is also something that will delight many online players.

2. Ski-sharing networks
Networked slices are different networks built on top of the underlying mobile network. In practice, this will mean that, for example, health services, industrial areas and zones for autonomous vehicles will each have their own customized network – but on the same mobile network.

– 5G will be so good that companies can have their own data connection configurations tailored to the individual business. The mobile network no longer becomes “one size fits all”.

3. Guaranteed quality of service
With 5G, it is possible to offer guaranteed service quality, or Quality of Service (QoS).

For example, many players, including Telenor, are working to facilitate self-driving, autonomous vehicles. When these roll out on Norwegian roads, it is crucial that they always have a secure, stable and fast connection through the mobile network.

– Imagine a surgeon performing a remote operation using a robot. It will require extremely fast response time from the network, but with 5G this will be possible.

In short, the time it takes for large amounts of data to be sent back and forth in the 5G network will be close to zero, which opens up an enormous number of opportunities that depend on real-time data – something that is not possible today over the 4G network .


4. “IoT” – everything is connected to everything
You may have heard the term before? The Internet of Things, or the Internet of Industry Things, has been talked about for a while now. It is simple to imagine that billions of devices, sensors, machines and things are connected to the Internet at any given time.

Admittedly, this is also being rolled out in today’s 4G network, but the 5G network will have the capacity to handle even more devices.

– Every Sunday when I cook in the oven, I think how nice it would be to be able to sit on the sofa and control the temperature in the oven and have a complete overview of the cooking.

With the 5G network we will see that more and more things are connected to the network, which can give us a more efficient and comfortable everyday life.


5. Full use of VR and AR

Because of the huge amount of data virtual reality equipment requires, many believe that 5G will be able to bring out the full potential of the technology – as data can be sent back and forth between the screens and a real-time server. The same applies to so-called augmented reality (shortened to “AR”).

VR includes technology that closes the outside world and lets you unfold in an artificial reality. With AR you can add digital elements to the reality we actually live.

The popular game Pokémon Go is perhaps the best known example of AR technology. As the technology advances, for example, you can get traffic info in a corner of the windshield as you drive home from work, and you and the pod can build complicated Minecraft structures in the park using their own set of AR glasses.

– Many people have the belief that glasses that can do different things will become a reality.


You must know these 5G terminologies:

Network Slicing: The mobile network can be divided into separate networks that work independently of one another

QoS: “Quality of Service”, security against outages, errors and delays

IoT: “Internet of Things”, billions of devices are connected to the web

VR: “Virtual Reality”, technology that encloses the user in an artificial reality

AR: Augmented Reality — augmented reality, adds digital elements to the real world

IoMT:”Internet of Medical Things”, billions of medical related devices are connected to the web

IoIT:”Internet of Industrial Things”, billions of industry devices are connected to the web

why 5g is the future
Artificial Intelligence in Telecom – From Hype to Reality – AI

Artificial Intelligence in Telecom – From Hype to Reality – AI

Surprising Ways Telecom Companies Use Artificial Intelligence

AI is having enough hype through media, researchers and vendors. Innovative organizations are putting a lot of efforts in AI research to make full benefits of it.

We all know about Sophia a social humanoid robot developed by Hong Kong based company Hanson Robotics. She looks quite human like and has been in talk shows a lot. She has also been given citizen ship in Saudi Arabia as being first artificial intelligence based human robot. She is being consider as intelligent but in reality, she is thoughtless whose intelligence is very basic. She can read a manuscript, on stage can perform a speech and she can answer questions that are pre-programmed. But If we ask her question out of script she can’t answer. Why is it so? Why is the gap here?

Well there is a basic difference of implementation AI.

Artificial General Intelligence

Machines that almost same level of intelligence that humans have, are expected in AGI products. This is what we all are expecting.

Artificial Narrow Intelligence

Machines have ability to perform specific tasks extremely well. These are computers that are trained to do simple and basic level of tasks only more efficiently than that of humans. This ANI based products are more in utilization by industry now.  i.e a machine that is trained to identify objects in images

A computer that can identify brain tumor cannot detect tiger in a picture because it has not been trained on that.

And certainly that’s the kind of AI, organizations like Telecom operators are trying to implement.5G  , IoT and big data will be handled by Narrow AI models to perform small task efficiently and fast.

Operators can train AI models such that, input from big data and after processing give expected results about customers. Mobile operators are also training the network based on alarms to predict whether there will be a failure or not. We just need to in put this data to AI model to learn and give out put as per desired results. It will also learn the relationship that if a new related input is given then it can predict the answer.  Similarly a new customer is added then machine will learn that client will churn or not.

This is very basic application of AI today in mobile telecom industry.

One more interesting and quite effective application of AI in telecom is dynamic carrier allocation.

 Artificial Narrow Intelligence trained model can add or remove additional carrier based on learning from previous weeks trends using capacity. No more manual allocation and wastage of resources.

Another example of machine learning using AI is

Automatize Customer Care:

Machines are trained to identify patterns in the text and predict problems for better solution offers.  

This is pure automation, quick and competent customer support application.

Challenges:

Challenges that mobile operators are facing at the moment is, they are not having enough samples of issues to predict more accurately.Only those queries are addressed well where model is having high volume data.

These are very concrete examples that are being used in Telecom companies today.We are seeing that AI will be a part of all domains in near future to automate tasks and improve productivity whether in Network, Customer care, Marketing, HR or Finance etc.

How to Adapt AI in Mobile industry:

First of all companies need to upgrade the technology stakes, upgrade the infrastructure and competence to work with AI. Especially need to fix the first mile and last mile of AI.

1st mile means data readiness. To be able to collect, store, process to make available the data for training AI models.

Last mile is infrastructure on which AI based models work and utilization of predictions from AI models to operations.

So deploying AI in Telecom sector effectively, mobile operators needs to work on refine enough big data and then focus on applying outputs in business operations.

Key barriers that must be solved and must have things for AI readiness:

  • Agile data Access Processes.
  • Experimental platform with the right tool.
  • Culture for experimentation and failing.  
  • Domain and AI expert collaboration / cross function operational model
Secret Methods of Applying Text Analytics ( AI and Machine Learning Application )

Secret Methods of Applying Text Analytics ( AI and Machine Learning Application )

Findings of 007 Agent in Textual Analysis

According to Deloitte forecasts, 80 of the top 100 largest developers in the world will use cognitive technologies (text analysis of natural language, speech recognition, neural networks, etc.) already in 2019, which is 50% more than in 2018.
What if your company is not in the top 100? And what if you do not understand anything about text analytics and Big Data technologies? Communications in the Big Data market so far is more like a huge secret, where you need to be 007 agent to figure out who plays what role. Almost all market players talk about intelligent data analysis systems, convenient visualization, cloud solutions, machine learning, language definition, etc.

And what are the differences? What questions should you ask yourself first if you intend to implement text analytics Big Data and if you do not have a technical background? How exactly can these technologies be applied to your business? Let’s figure it out.

What tasks does text analytics solve using Big Data ?

The concept of “text analytics” is not as popular as the phrase “Big Data”. However, it is in the format of unstructured text that about 80% of all accumulated information is presented, according to a report by International Data Corporation. What to do with it and how?

Dmitry Torshin, IT Director for Investment and Vice President of Aplana, is sure:“The use of modern technologies based on text analytics is one of the most important tasks that heads of developing companies in Russia should set themselves. Their colleagues from developed countries have already done this, and we all are already using it, not even always realizing it. An x.ai virtual secretary has already been created to coordinate meetings (being a program, but it does not give out anything except a mail address – it perfectly answers questions and suggestions of people in correspondence). The App in the Air Chat on Facebook Messenger gives me the opportunity to learn in simple language what I can take with me on a plane in a particular country and what cannot, and find the flight I need. And the latest version of Apple’s desktop operating system, macOS, which came out just the other day, contains Siri and a search that lets you ask your computer to find “documents, which Petya sent me last week. ” People instantly get used to it, and if tomorrow your business is not able to communicate in human language with a client as well, then it will be seriously squeezed out with competitors ”.

Nevertheless, the most common cases of the applicability of text analytics can be found in the advertising market, in the banking industry, as well as in online retail (where this trend is only emerging, but the benefits of using it are already obvious).

What tasks can be solved by text analytics of unstructured data in advertising and customer service:

– Compilation of brand loyalty ratings,
– Increase in CTR by increasing the effectiveness of native advertising (matching content of the placed advertising),
– Content analysis (tagging and classification) to create the next sub-product or adjust the current one,
– Implementation of text analysis technologies in chat rooms for community management,
– Automatic identification of various kinds of entities and frequency analysis of words,
– Control of tonality of brand references as an indicator of the company’s health,
– Detection of trends at the time of their inception,
– Improving the effectiveness of loyalty programs (by monitoring not only the public space, but also the analytics of text data of chats, messages of call centers, email messages).

Banks are perhaps the leader in applying analytics to unstructured Big Data. Says Sergey Dobridnyuk, Director of Research and Innovation, Diasoft Systems, who are actively studying the banking sector:“My opinion is that trying to structure everything is a dead end. Up to 80% of daily information “digitized” by humanity is contained in an unstructured form. And the reason here is the complexity of both data and classifier systems. For example, to classify sales receipts for PFM systems, you will need to create a classifier with at least 1.5 million SKU headings. This is an unrealistically large dictionary in which it is easy to make a mistake: the great Pushkin had a vocabulary of about 30 thousand words. And IT successfully fights this complexity – there are hundreds of data management systems (DBMS) in NoSQL technologies – for which unstructured data is their native element. Algorithms are being greatly improved – for example, multilayer neural networks, Bayesian neural networks find connections and process texts, speech, images thousands of times faster than 10 years ago. Very high-quality and open source free software libraries have appeared – which make these technologies available to all comers. A breakthrough technology today is Machine Learning, when causal relationships are established by a computer based on statistical analysis, and even a person cannot explain logic – preferring to consider it an unknowable “black box”. All this is important in order to offer the client comprehensive services based on behavioral models, collected customer experience (CX). And the quality of the offer is constantly improving due to continuous monitoring of the client, all his “digital traces” in a structured and unstructured form. The intrigue also lies in the fact that this can be done not only by banks – but also by retailers, telecom operators, suppliers of services and goods, already knowing the client and offering him financial services no worse than the “average bank”. Therefore, classical banking is today in a zone of deep turbulence and a rethinking of its activities. ”

And the director of IBS Data Lab, Sergey Zablodsky, does not doubt the “real” analytics of unstructured data: “The question of the applicability of BigData analytics in business solutions today is no longer there. Rather, there is the task of doing this effectively. And for examples of effective solutions you do not need to go far – look at Uber, Airbnb, Netflix, Walmart. And these are only those names that are heard. All of them actively and successfully use BigData analytics in their business solutions, and for some, the entire business is based on BigData analytics. For example, the likelihood of commercial success of the series produced by Netflix reaches 70%, while the average market probability is only 35%. ”

Where to start and what is important to know for implementing text analytics ?

The most well-known companies in the field of Big Data and linguistics – mainly due to loud cases and the presence of visualization (interface) – have become social media and media monitoring companies (Brand Analytics, Brandwatch, Radian 6, Cribrum, etc.). However, the text analytics industry is not limited to this, but on the contrary, it becomes extremely difficult to understand the differences between the proposed solutions.

First of all, when choosing a text analytics solution, you should think for yourself what characteristics are important to you (provided that you have already decided that you will use text analysis technologies to solve a specific business problem).

Answer the questions below:

1. Do you really have big data or not? Are there really a lot of unstructured data among them?
2. What is more important for you: depth of analysis or speed?
3. Texts in what languages ​​do you need to analyze? Each solution on the market has its own technical features of text analysis and language definition. All international corporate machine learning solutions work perfectly with the English language, but in the case of Russian there are many problems. Rich and powerful, so to speak!
4. Are you ready to export data?
5. In general, do you want a technology or a finished highly specialized product?
6. Do you need data collection or just text analysis of big data, or both?
7. Is it fundamentally a solution to your internal circuit or a cloud-based solution through the REST API?
8. Do you have the resources to visualize the analyzed data?
9. Which of the main areas of text analytics do you need: search (information search methods) or descriptive / predictive analytics (text mining and tonality determination)?
10. Do you need to extract commercially useful knowledge from text online?
11. Do you have professionals in the team who are able to correctly interpret the result of the analysis, introduce the technology, create a product, or do you expect this from the technology supplier?
12. Finally, what budget are you willing to invest in such decisions? It must be understood that the maximum benefit from the analysis of big data can be obtained with a long-term analysis (that is, evaluate the results of analysis in time), and this is a subscription model, and not a one-time project.

Who has what method?

So, you were more or less able to answer the questions listed above. The next step is choosing a partner. Unstructured information analysis solutions can be conditionally divided into 3 types:

– Finished products based on text analytics technologies: not for a mass audience, and therefore quite expensive and “tailored” for a specific segment of B2B clients.
– Point solutions-products at the junction of text analytics and big data for the mass-market segment, if I may say so in B2B: simpler to implement, designed for different B2B segments.
– Modular text analytics technologies: perhaps the most flexible in implementation, suitable for a wide range of tasks – such a cube in the Lego text analytics constructor for business.

The first group includes solutions really from the field of artificial intelligence, which can perform not only the tasks of text analytics, but also in general, provide cognitive services and their mix. For example, IBM Watson, officially launched in 2007, operates big data regardless of the type and format of data, has the ability to self-learn, and is suitable for quickly finding answers to questions. On their website they provide a demo for subscription.

Both startups and very targeted products of well-known corporations fall into the second category. For example, in the summer of 2016, ABBYY announced the launch of Findo, a search assistant for mail messages, files and documents in the clouds. And in 2014, ABBYY launched Compreno – an intelligent search and identification of “essence” in texts. Of the non-corporate solutions on the market, there are innovative companies / startups such as Textocat (also offering smart search) and the product “chat bot”. SAS also released two key solutions for text mining and tonality analysis: SAS Text Miner and SAS Sentiment Analysis.

Among modular technologies, players such as Yandex Data Factory and EurekaEngine are actively present on the market. Both help companies make commercial use of the accumulated data: create end services in existing business processes of companies instead of implementing software and visualizations. YDF uses corporate experience and machine learning technologies, EurekaEngine uses high-speed text analytics, especially for the Russian-speaking space, because the company has its roots in Russia (which, by the way, is used by Brand Analytics, one of the leaders in the market for social media and media monitoring services, which took 1st place by quality among social media monitoring systems in the TECH INDEX 2016 ranking by AdIndex).

Advertisers, especially DMP systems and advertising auditors, also have their own developments in the field of text analysis, but they are mainly used for their own internal tasks: segmentation, more targeted targeting, semantic comparisons of audiences (for example, audiences of mobile applications), etc. d. As you know, the devil is in the details: almost everyone has problems with the accuracy of the analysis and the inability to separate the advertising content of the text block from the text of the article in the media, as well as the further output of the product.

Conclusions

What does client think about the usefulness of text analytics in solving business problems? Ivan Tretyakov, managing partner of the Association A.R.Z.A.M.A. and POSonline service:

“In an era of growing consumption, as well as demand for the quality of services, business (in particular the banking and retail segments) began to look more deeply at the root of how to be closer to the client and make him more loyal. Big Data tools – analytics and, in particular, analysis of text arrays already today show amazing results: you can adjust your service based on the feedback of people in the media, chats, forums; You can offer people interesting promotions / discounts by studying their factors of demand and interest in specific product groups / brands; you can expand your own list of services offered or lower the loan rate by studying the behavior of your customers in the Internet.

Text analytics can be applicable not only for a business concentrated in the Internet space, but also for offline players, for example: by analyzing the behavior / user reviews on the Internet for certain goods / services and armed with geolocation services to work with a potential audience – you can offer them interesting Products / services / solutions are already in offline space. For example: courses, travels, workshops, etc. And thanks to the availability of ready-made SAAS services for text analytics, the business will receive a strong tool to grow its profits and increase satisfied customers. ”

Must Have Marketing Skills to Survive in The Age of AI

Must Have Marketing Skills to Survive in The Age of AI

10 Skills Without Which A Marketer Cannot Survive in The Age of AI

It happened. We live in an age of artificial intelligence. I can’t believe it, right?

It is high time for marketers to accept this fact and begin to prepare for the inevitable changes in the technology era.

Already, machines are actively learning to recognize images and speech, to predict the likelihood of the development of certain events and make decisions. That is to do our work.

Today, the most effective brands are more than twice as likely as competitors to use AI in their marketing processes. Artificial intelligence helps companies to increase sales, indicators are growing due to the personalization of experience .

Over the past 5 years, the number of jobs requiring AI knowledge has grown by 450%.

And artificial intelligence will continue to conquer the field of marketing. Are you ready for this?

In today’s article, we’ll talk about 12 skills without which a marketer cannot survive in the AI ​​age.

1. Flexibility
If you don’t start to adapt today, you will soon be behind your competitors. Just look at how many companies are already using AI, and how many plan to implement this technology in the future: AI is the fastest growing marketing technology. It is expected that over the year it will grow by 53%.

If you want to succeed, you need to adapt. Do not rely too much on time-tested strategies. Feel free to experiment and test new technologies.

2. Sociability
It’s not new to marketers that developed communication skills make a significant contribution to business success. It is important to be able to convey your thoughts to employees, customers and other people with whom you have to communicate every day.

In the age of AI, communications are becoming an even more significant element of business. After all, not one, even the most advanced artificial intelligence, is capable of replacing live communication.

Do not delegate communication with clients to robots at 100%. In the age of high technology, the human face of the brand will become a major competitive advantage.

3. Budget allocation
The introduction of artificial intelligence is not cheap. For this reason, most brands do not use solutions in AI marketing:

For what reason you are not interested in implementing AI solutions ?
If you decide to incorporate new technology into your strategy, you cannot do without planning and budget allocation skills.

Try to find ways to cut costs in other areas to enter the new century before the competition.

4. The ability to analyze big data
AI will open up access to huge amounts of data that are important to be able to analyze.

According to research, 29% of brands use artificial intelligence to automate data analysis. 26% use AI to analyze operational effectiveness. As a result, business owners receive large amounts of information, on the basis of which it is necessary to draw conclusions and make decisions. Are you ready for this?

5. Programming Skills
To use artificial intelligence, you do not have to be a programmer. However, knowledge in this area will not be superfluous, for sure. For example, they will help to save on the call to a specialist.

You will have enough basic skills to configure the collection of data that you need.

Often AI is used to identify patterns. If you are good at programming, it will be much easier for you to understand the features of this field of application.

Far from all this? You can easily find basic information on the Internet, for example, on Codecademy . Both courses for beginners in programming basics and specialized training materials in data science will come in handy.

6. Content creation
Content is king, right? The main goal of modern business is to create effective content both from the point of view of users and search engines. Any marketing strategy is based on the generation of content.

The competent use of artificial intelligence will allow you to make your articles, posts, videos, photos, audio, email messages even better. For example, some brands due to AI make Facebook ads even more relevant for different user groups. And this is only one of hundreds of areas of application of artificial intelligence for creating and promoting content.

7. Security
Over the past few years, we have only heard about constant leaks of information in large corporations. These messages significantly damage the reputation of brands.

Do you want your customers not to worry about the safety of their personal data? Use AI with caution.

Consumers believe that artificial intelligence can make it harder for a business to secure online. Develop this myth by conducting thematic campaigns, and always responsibly treat user data.

8. The spirit of competition
Marketing is a high stakes game. You constantly have to fight with other companies for users.

After the massive introduction of artificial intelligence, competition will only intensify. Without the spirit of competition, one cannot survive.

84% of marketers are confident that AI will help them outperform competitors. Find out, and your rivals are already introducing new technology?

9. Delegation and time management

The community is hotly debating the possibility of replacing people with robots in most jobs. But this does not mean that AI should be considered as a threat. Rather, it is a dream assistant for any marketer. After all, with its help you can automate many tasks.

You no longer have to load workers with the work that a computer can easily do. They will have more time to solve creative problems.

10. Thirst for knowledge
To survive in the age of AI, it is important to be able to learn. The benefit for this today is not necessary to leave the house. There are many training courses and webinars available online.

Technologies are constantly evolving, so you should closely monitor the news, expand your knowledge and listen to the opinions of experts.

Does your business need artificial intelligence to survive?

Naturally, it is not so necessary as a presence in social networks, your website and the ability to accept online payments. From this point of view, it will not be easy to convince yourself of the need for such a serious step. But in this case, it is important to have a broad outlook and look at things in the future.

If even now you can’t afford the introduction of technology, you need to start closely monitoring it today. After all, artificial intelligence is the future, and not just marketing.

Cutting Edge Technologies That Will Change Marketing Industry Forever

Cutting Edge Technologies That Will Change Marketing Industry Forever

It is difficult to imagine a marketing field that modern technologies would not significantly change. Companies that rely on artificial intelligence, virtual reality and voice search, gain an advantage over competitors and let them create future promotions with extra ordinary results.

We have listes 10 leading marketing technologies and the possibility of their application in companies of various sizes. Which of them will you choose to transform your strategy?

10 Cutting Edge Technologies Changing Internet Marketing

1. Big data

• Improves the quality of customer data collection for fine-tuning advertising campaigns.
• Helps evaluate campaign performance.
• In the near future, big data will allow creating attribution models to assess the impact of each channel on conversion rates, customize programmatic ads and optimize video marketing.

2. Artificial Intelligence

• Finds valuable patterns for more effective targeting and prediction of consumer behavior.
• Used by search engines to analyze queries and select the appropriate content.
• Based on artificial intelligence, platforms for online chat are created that help to automatically collect customer information and solve problems on demand.
• AI-based technologies deeply analyze trends, create detailed customer profiles, and help develop successful personalization strategies for better customer focus.

3. Machine Learning ( ML )

• It is used in audience segmentation and is embedded in analytics systems to track anomalies and analyze large volumes of data in real time.
• Robots have learned to create content. Banner advertising, email campaigns, posts on social networks are generated in different formats for different channels. After analyzing enough data, machines can create and change headers to increase efficiency.

4. Bots

• Not only an effective tool for communication, but also a channel for round-the-clock interaction with the brand.
• Often used in sales and support, help find and recommend products.
• Soon they will be able to remind of repeated purchases through voice assistants.
• Communication with the chat bot can occur on several devices, be omnichannel.

5. Voice Search

• Marketers use voice search to collect information about device users through search queries, keywords, applications, or voice dialing.
• Soon, voice search will be integrated with SEO. Marketers need to learn how to optimize content for conversational queries.
• The technology has every chance to change the approach to advertising on the search and organic promotion of content.

6. Virtual and Augmented Reality

• Both technologies create an impressive experience that affects feelings and emotions.
• They expand the experience of product testing, brand engagement, and shopping.
• They bring offline stores and ecommerce closer, gradually blurring the line between real and virtual interaction.
• Can be used for storytelling and creating interactive brand content.

7. Internet of things ( IoT ) and wearable devices

• Used to collect information about users: their habits or preferences. The more connected devices a person uses, the more marketers have more opportunities to contact him with an actual offer.
• Wearable devices transmit information on the biological state of consumers to the Internet.
• Biometric data can be used to analyze consumer interactions with the brand.

8. Blockchain

• Using blockchain technology, marketers can motivate consumers to view ads and interact with content.
• Decentralized applications based on blockchain technology can compete with Apple and Android platforms and support a new cooperative economy around the world.

9. Beacons

• Gathers detailed information about the visitor to optimize the shopping experience and helps create personalized campaigns based on movement data.
• Ecommerce companies can use localization to target potential customers within a certain radius of the sensors.
• Combines online and offline presence and provides a consistent experience.
• It helps to determine which campaigns attract attention and show only relevant ads to each client.

10. 5G

• A faster connection allows you to load pages faster, reduce bounce rates, and increase CTR and ROI.
• Enhances display capabilities using VR and AR for an engaging demonstration of offers.
• Allows marketers to collect data in real time to optimize campaigns and local promotion.
These technologies significantly affect marketing and business, including:

• Data collection
• Data analysis
• Content Creation
• Content distribution
• Personalization
• Targeting and placement
• Customer service

Over All Digital or Internet Marketing is going to have huge exponential positive impact with the deployment of these amazing latest technologies.

Technology is evolving and becoming more accessible. Changing the industry under the influence of technology is happening now, you need to have time to master promising areas.

Incredible Examples Of AI And Machine Learning In Practice

Incredible Examples Of AI And Machine Learning In Practice

Artificial intelligence and machine learning are some of the most significant technological developments of recent times. However, they still remain underestimated in terms of application throught 2019.

10 Incredible examples of AI And Machine Learning ML in practice. We want to see how machine learning is applied in real life?

Here we have compiled 10 companies that effectively use new technologies in their strategy.

1. YELP - IMAGE CURATION

Although Yelp, a popular reviews site, doesn’t seem like a high-tech brand, it actively uses machine learning to improve its user experience.

Classifying images into façade / interior categories seems like an easy task for a person, but a computer can handle it quite difficult.

Photos are important for Yelp no less than user reviews, which is why the company makes a lot of efforts to improve the efficiency of working with images.

A few years ago, the brand decided to turn to machine learning and first applied photo classification technology. Algorithms help company employees select categories for images and put down tags. The contribution of machine learning is hard to overestimate, because the brand has to analyze tens of millions of photos.

2. PINTEREST - CONTENT SEARCH

The main function of the Pinterest social network is curation of content . And the company is doing everything possible to increase the efficiency of this process, including the use of machine learning.

In 2015, Pinterest acquired Kosei, a company specializing in the commercial use of machine learning (in particular, content search and recommendation algorithms).

Today, machine learning is involved in every aspect of Pinterest’s business operations, from moderation of spam and content searches to monetizing ads and reducing the number of unsubscribes from newsletters. Not bad.

3. FACEBOOK - THE ARMY OF CHATBOTS

Facebook Messenger is one of the most interesting products of the largest social platform in the world. All because the messenger has become a kind of chatbot laboratory . When communicating with some of them, it is difficult to understand that you are not talking to a person.

Any developer can launch it on the basis of Facebook Messenger. Thanks to this, even small companies are able to offer customers excellent service.

Of course, this is not the only machine learning application on Facebook. AI applications are used to filter spam and low-quality content; the company also develops computer vision algorithms that allow computers to “read” images.

4. TWITTER - NEWS FEED
One of the most significant changes in Twitter in recent years is the transition to a news feed based on algorithms.

Now users of social networks can sort the displayed content by popularity or by publication time.

The basis of these changes is the use of machine learning. Artificial intelligence analyzes each tweet in real time and evaluates it according to several indicators.

The Twitte algorithm primarily shows those entries that are more likely to please the user. Moreover, the choice is based on his personal preferences.

5. GOOGLE - NEURAL NETWORKS

Google has an impressive technological ambition. It is difficult to imagine the field of scientific research in which this corporation (or its parent company Alphabet) would not have contributed.

For example, in recent years, Google has been developing aging technologies, medical devices, and neural networks.

The company's most significant achievement is the creation of machines in DeepMind that can dream and create unusual images.

Google is committed to exploring all aspects of machine learning, which helps the company improve classical algorithms, as well as more efficiently process and translate natural speech, improve ranking and predictive systems.

6. EDGECASE - CONVERSION RATES

For a long time, retailers have been trying to combine shopping in online and offline stores. But only a few really succeed.

Edgecase uses machine learning to enhance its customer experience. At the same time, the brand seeks not only to increase conversion rates, but wants to help those customers who have a vague idea of ​​what they want.

By analyzing the behavior and actions of users that indicate their intention to make a purchase, the brand makes online search more useful and brings it closer to the experience of shopping in a traditional store.

7. BAIDU - THE FUTURE OF VOICE SEARCH

Google is not the only search giant that masters machine learning. Chinese search engine Baidu is also actively investing in the development of AI.

One of the most interesting developments of the company is Deep Voice, a neural network capable of generating synthetic human voices that are almost impossible to distinguish from real ones. The system can imitate features of intonation, pronunciation, stress and pitch.

The latest Baidu Deep Voice 2 invention will significantly affect the efficiency of natural language processing, voice search and speech recognition systems. It will be possible to apply the new technology in other areas, for example, interpretation and biometric security systems.

8. HUBSPOT - SMART SALES

HubSpot has long been known for its interest in technology. The company recently acquired Kemvi, a brand specializing in machine learning.

HubSpot plans to use Kemvi technology for several purposes: the most significant is the integration of machine learning and natural language processing DeepGraph with an internal content management system.

This will allow the company to more effectively define “triggers” - changes in the structure and management of the company that affect day-to-day operations. With this innovation, HubSpot will be able to more effectively attract customers and provide a high level of service.

9. IBM - NEXT GENERATION HEALTHCARE

The largest technology corporation IBM is abandoning an outdated business model and is actively exploring new directions. The brand’s most famous product today is Watson Artificial Intelligence.

Over the past few years, Watson has been used in hospitals and medical centers where it has diagnosed certain types of cancer more effectively than oncologists.

Watson also has tremendous retail potential where it can serve as a consultant. IBM offers its license-based product, which makes it unique and more affordable.

10. SALESFORCE - SMART CRM SYSTEMS

Salesforce is the titanium of the technology world with a significant market share in customer relationship management (CRM).

Predictive analytics and lead assessments are the main challenges of today's online marketers, which is why Salesforce places high stakes on its Einstein machine learning technology.

Einstein allows companies that use Salesforce CRM to analyze every aspect of their customer relationship - from the first contact to the next points of contact. Thanks to this, they can create more detailed profiles and determine the most important points in the sales process. All this leads to a more effective assessment of leads, improving the quality of customer experience and expanding opportunities.

The future of machine learning
Some of the forms of machine learning listed above seemed fiction ten years ago. Moreover, each new discovery does not cease to amaze today.

What AI and Machine Learning trends await us in the near future 2020?

1. MACHINES THAT LEARN EVEN MORE EFFICIENTLY
Very soon, artificial intelligence will be able to learn much more efficiently: machines will improve with minimal human involvement.

2. AUTOMATION OF THE FIGHT AGAINST CYBER ATTACKS
The rise of cybercrime is forcing companies to think about defenses. Soon, AI will play an increasingly important role in monitoring, preventing and responding to cyber attacks.

3. CONVINCING GENERATIVE MODELS
Generative models such as those used in Baidu from the example above are pretty convincing today. But soon we will not be able to distinguish cars from people at all. In the future, algorithms will be able to create pictures, imitate human speech and even entire personalities.

4. QUICK TRAINING
Even the most complex artificial intelligence needs a huge amount of data for training. Soon, machine learning systems will require less and less information and time.

5. INDEPENDENT ARTIFICIAL INTELLIGENCE
For a long time, people have been wondering if artificial intelligence can be dangerous to humans.

In June of this year, Facebook’s artificial intelligence research team (FAIR) experts decided to disable one of the systems they created, as the bots began to communicate in their own language, which was incomprehensible to humans. Experts call for the introduction of regulation of this area of ​​technology in order to avoid the threat of artificial intelligence getting out of control.

In the future, this may lead to restrictions and even a slowdown in the development of this area. In any case, it is important to use new technologies for the benefit of mankind, and not to the detriment. And this requires strict regulation of the industry.

YOU KNOW Over 50% of advertisers will use AI in 2020

This conclusion was reached by the authors of the research “AI in marketing” from the Segmento advertising platform, who interviewed representatives of the 300 largest advertising companies. 20% of companies said they already use artificial intelligence for marketing purposes, they will join another 32% next year. The remaining 48% of companies refuse to use the technology because they lack the competencies to make a positive decision (33%), it will be difficult to find experienced specialists in the field of AI (27%) until they assess the possibility of using the technology (18%).

The most popular AI-based tools are programmatic purchase of media advertising and retargeting - returning a visitor to the advertiser's website (48% each), chatbots and big data analysis for making management decisions (38% each), personalizing a website or mobile application (29% ) Almost a quarter of companies predict AI customer satisfaction. The technology is least used to offer consumers individual prices for goods and services (5%). Companies also use AI to automate call centers and improve the quality of goods and services.

As a rule, advertisers turn to advertising agencies (47%) and AI-specializing companies (33%) to integrate relevant solutions into the marketing practices of their companies.

“In the case of artificial intelligence, it can be stated that so far the technology solves specific applied problems pointwise, and their spectrum is very limited. For a more substantial and comprehensive integration of AI, companies will have to conduct a global audit of their business processes.

Machine Learning Trend To Find Bugs

Machine Learning Trend To Find Bugs

Can we find bugs in program through Machine Learning?

Automated bug detection before the actual program running is increasingly popular feature researchers are looking for.Programming errors and other code quality issues determination is in search of big lead here i.e finding errors in the Linux kernel before the code is incorporated, probably not but can only be possible with machine learning.

Using AI, Linux kernel developer Sasha Levin looks for patches for the the Stable and Long Term Stable (LTS) trees that improve code. But did he use the ML system to find patches that contain bugs? It’s a difficult task for Levin, but he has some clues as to how that could be done.

The Microsoft employed developer Sasha Levin maintains together with Greg Kroah-Hartman the so-called stable trees of the Linux kernel. Among other things, Levin uses a machine learning approach to find the necessary patches for improvement . As the developer reported in his presentation at this year’s Open Source Summit Europe in Lyon, he had been repeatedly asked because of his work, whether it could not be found bugs before they are even incorporated into the kernel. The answer is, according to Levin, but anything , as he presents in a detailed analysis.

Because, as many developers know, detecting bad code is not a manageable task. Although there are already a variety of tools for finding errors, such as static code analysis. But from the point of view of Levin, the biggest source of error in the development of the Linux kernel is its development process itself. The developer tries to underpin this with his own analysis.

Objective analysis is difficult to implement

From his personal experience as a maintainer. Levin knows this review, that is, third-party checking of the code, as well as code testing, help prevent the introduction of bugs. It plays quite a role, who does the review, how much time it takes or even how extensively the possible disputes are formulated.

Although it is difficult to actually quantify these and other things. This applies above all to the question as to what should be considered as a bug in the sense of the original question and investigation. Nevertheless, Levin has tried to translate some of these considerations into a machine-learning model using a preselected set of code contributions to the kernel.

Of course, the model inevitably has weaknesses and can not be used directly to actually find faulty code before it is entered into the main branch / tree of the kernel. For Levin, however, the investigation thus carried out offers some very important clues.

Fast patches just before the deadline have more bugs

Probably the most important finding here, according to Levin, is that the probability of introducing errors in contributions is three times higher than normal if the code is added to RC kernels late. This seems counterintuitive, as after a two-week phase to submit new features for the upcoming Linux version (Merge Window), a mostly eight-week trial phase with bug fixes and release candidates (RC) follows, before a new Linux version appears ,

According to Levin, this result confirms his assumptions about the reviews. Thus, new features and major changes often go through a long review phase and the patches are usually discussed extensively. However, in the late RC phase of kernel development, the process of implanting is much faster and often there is no review at all.

Levin found a lot of patches for this development phase, the code of which was written, submitted and entered on a single day. Of course, with such a rapid development, the potential for error increases.

Whether and what follows from this realization but in the long term for the development process of the Linux kernel is not really clear for Levin. He had some ideas, but these were difficult to implement. This includes a real freeze phase in the development to extensively test the innovations. Possibly shifts the inclusion of short-term patches but only further back.

Similarly, Levin could imagine a kind of standardized approach to accepting patches in the main branch. As a prerequisite for a recording this could be a minimum number of days that the patches in the Linux Next branch must be present before inclusion in the main branch. Similarly, extensive reviews or tests could be forced or so-called sign-off tags. The latter in this case would be roughly “approved by” .

All these requirements would, according to Levin with a not inconsiderable share of developers and maintainers encounter resistance and are therefore not feasible.

Researchers are also using machine learning for finding trends. Here are takeaways From The First Operational ML Conference USENIX OpML 2019

 

2019 Onward: Everyday Is Information Cyber Security Day

2019 Onward: Everyday Is Information Cyber Security Day

5G, AI and IoT driven Industrie 4.0 has biggest challenge ahead and that is implementing industrial graded security systems. Smart cities, autonomous vehicles and Nuc plants just imagine the importance of computer network systems security and impact of any cyber attack. World cyber security day, week and month November 2019 is here to highlight the importance of this challenge that we are going to face in future.Talks, demo, conferences, and an AI, 5G,IoT Hackathon will compose the month of cyber security.

During all these awareness efforts we are here, Windows ‘BlueKeep’ CyberAttack Is Happening Right Now.

Even That U.S. Government has warned us about the devastating risks of BlueKeep a security vulnerability that was discovered in Microsoft's Remote Desktop Protocol implementation, which allows for the possibility of remote code execution.

As a vulnerability such as Wanna Cry describes Microsoft Bluekeep. Now security researchers discovered the first malware that exploits the gap. However, this is still a long way from the worst-case scenario.

Already in May, Microsoft vigorously warned of a vulnerability that could spread like Wanna Cry independently. For the first time, security researchers Kryptos Logic have been able to sift through malicious software that exploits the Bluekeep gap. However, it seems almost harmless to the potential of the vulnerability.

Since Microsoft released security updates in May for all supported and even unsupported operating systems, there was silence before the storm. A wave of attack on unprotected devices that did not play the security updates was just a matter of time. Gradually, security researchers also released proof of concepts (PoC) or even exploits for pentesting software. But the big attack was slow is coming.

For the first time, security researchers discovered malicious software in the wild, exploiting the Bluekeep vulnerability. In a honeypot, a computer with vulnerabilities run by security researchers to detect and analyze malicious software, they discovered malicious software that used the loophole to steal computing power. This used the malicious software for cryptomining. However, the malicious software crashed the affected Honeypot, so security researchers doubt the reliable functioning of the malicious software.

The Bluekeep Cryptominer is not a worm


The Bluekeep vulnerability allows malicious code to be executed on an affected Windows system without the need for system authentication or user interaction. A computer worm could self-propagate through the vulnerability from vulnerable computer to vulnerable computer. However, according to the security researchers, the malicious software that has now been discovered does not spread on its own. Instead, the attackers scan for vulnerable systems and then attack them.

One reason for the absence of a Blueekeep worm could be Microsoft's handling of the vulnerability. Security updates and warnings from Microsoft may have contributed to significantly reducing vulnerable devices. "Every month that passes without a worm being released, more people are turning to security updates and the number of vulnerable devices is falling," said security researcher Jake Williams Wired. That so far no attacker had exploited the gap on a large scale, could also be based on a cost-benefit calculation. There may be too little affected Windows machines, as that is worth the effort, explains Williams.

In contrast, Wanna Cry paralyzed millions of Windows machines in 2017 , leading to system failures at a number of companies . In addition to the scoreboards of the train denied many money, ticket and gas station machines the service. Calculator of the mobile operator Telefónica were also affected, and the car manufacturer Renault had stopped its production in some plants as a precaution. The Wanna Cry malware was based on a vulnerability in Samba hoarded by the US National Security Agency (NSA), leaked by the hacker group The Shadow Brokers .

AI vs. Human

AI vs. Human

AI vs. Human Intelligence is a fair matchup or not ?


Should we stop thinking AI vs. Human, Think AI With Human ?

Mostly experts are having of the view that artificial intelligence (AI) is completely an automated process without any human intervention, but in reality most of the input gained by AI based systems are from humans. Concern, that AI will replace human beings in the digital workplace, is more closer to reality and it is also likely that humans and machines will work together.

This time under the subject " ai vs human " another test is carried out by DeepMind on its artificial intelligence agents that were trained to play the Blizzard Entertainment game StarCraft II.

Test environment was Google-owned AI lab that is more sophisticated one and software, still called AlphaStar, is now grandmaster level in this real-time strategy game.It is capable of beating 99.8 percent of all human players in competition.

Starcraft AI is already among the best players.

Deepmind's AI Alphastar started small. Meanwhile she plays in the Grandmaster Ladder against the best human Starcraft 2 players. The system dominates Terrans, Protoss and Zerg - each as its own neural network.

Deepmind's AI system Alphastar is already superior to about 99.8 percent of all active players in the real-time strategy game Starcraft. Meanwhile, it plays in the competitive ladder system at the highest level: Grandmaster. Professional players who also play for prize money in tournaments move on this level. Interesting: Alphastar now dominates all three playable races - Terrans, Protoss and Zerg - and should have developed according to the developers against all three races tactics.

For each of the three quite different fractions, the program uses a self-contained neural network with its own weights and training data. The current version of Alphastar has also incorporated some predetermined limits. For example, the AI ​​does not see through the fog of war and has to explore the playing card, analyze the base of the opponent and develop strategies against it. In addition, the system may execute a maximum of about 264 actions per minute.

Since the summer of 2019 , Alphastar plays in the European ladder against human players. However, the program does not pretend to be undetected and plays with several agents in parallel. Some community members such as the Youtuber LowkoTV have probably already played against the program. Partly very unconventional tactics reveal the software. He was also struck by the fact that Alphastar agents always play exactly 50 games within a few hours at a time.

German Starcraft player Dario Wünsch - Liquid TLO - works with Deepmind and was involved in the training of Alphastar. "While Alphastar has excellent and precise control, it does not feel superhuman," he says. However, this software is not perfect yet. Although she can quickly play against different versions of herself and evaluate this, but there is a risk that Alphastar forgot ways of playing past matches again.

The team at Deepmind continues to work on his project. The developers even see the approach as universally applicable - for example in games other than adaptive computer opponents.

Future Technology Predictions 2020

Future Technology Predictions 2020

Future technology prediction is what every one has eyes on.Innovations are too quick just like rapid fire session.

Just go with the flow, as technology gurus aka CEOs of Microsoft, Facebook, Amazon, Google, Tesla, Telenor & Qualcomm have already envisioned for us. Every one is agreed on the this perfect combination:

Intelligent Embedded System, 5G, IoT and AI

Smart yet powerful processors based on embedded systems will enable 5G & IoT. Then of course Internet of Things will boost AI via 5G.IoT is AI enabler and going to change the current business scenario altogether. There are going to be new opportunities after applying this combination of cutting edge tech.

Even Elon Musk stepped further in AI application by directly connecting human brain with Neural AI based machine.After that bold initiative by Elon, Microsoft has decided to invest $1 billion in this artificial intelligence venture that plans to replicate the human brain using computers. So Future technologies are revolving around IoT and AI.

Bill Gates has picked a detailed list of emerging technologies that will rule by 2020.

The founder of the American company Microsoft Bill Gates compiled a list of ten breakthrough technologies in 2019 for the publication of the Massachusetts Institute of Technology MIT Technology Review.

Since 2001, the publication annually compiles a list of ten key technologies that should provide a technological breakthrough. As a rule, the top 10 technologies are determined by the editors, but this year Bill Gates was invited to compile the list.

“It was difficult to reduce the list to 10 positions. I tried to choose not only the technologies that will appear in the news headlines, but also those that will make 2019 a year in the history of technology development, ”Gates said.

The Microsoft founder identified the following key areas:

 
1. Creating robots that can more cleverly interact with the outside world.

 

In order for robotics to be used in mass production, it is necessary to increase the accuracy with which robots interact with the environment. The development of these technologies will lead to the fact that the robot can be used not only in industry (for example, when assembling electronic devices), but also in everyday life.

 
2. A new stage in the development of nuclear energy.

 

This year, several new generation IV nuclear reactors will be launched. Commercial use of such nuclear reactors will begin no earlier than 2020, but they will make nuclear power engineering safer and cheaper. There is also progress in the field of thermonuclear fusion, such work is carried out by General Fusion and Commonwealth Fusion Systems, but no one expects industrial application of controlled thermonuclear fusion before 2030.

 
3. Genetic tests that determine the risk of preterm birth.

 

Several companies are working on genetic tests that will identify the risk of preterm birth. For example, the startup Akna Dx expects to offer a blood test technology, testing at which will cost $ 10. These tests will help doctors identify groups of women in need of special monitoring of the progress of pregnancy.

 
4. Intestinal probe in tablet form.

 

Small devices that can replace gastrointestinal probes, providing the same accuracy of verification as well as the possibility of a tissue biopsy, have already been developed in the Tearney laboratory. It is planned that in 2019 these capsules will be reduced even more, which will allow their use even in the study of infants.

5. Individual cancer vaccines.

This technology is jointly developed by BioNTech and Genentech. The new technology will allow you to create individual vaccines for each patient, based on data obtained from a biopsy of a cancer.

 
6. Growing artificial meat.

 

People are unlikely to give up meat in the near future, so one of the promising areas is the development of technology for growing artificial muscle tissue of animals in bioreactors. Such studies are engaged, for example, scientists from the University of Maastricht in the Netherlands.

 
7. Carbon dioxide trap.

 

Methods of capturing carbon dioxide in the air are complex from an engineering point of view, but can be a real solution to the problem of global warming.

 
8. “Smart watch” with ECG support.

 

Wearable electronic devices that provide continuous monitoring of the cardiovascular system achieve the accuracy of medical devices.

 

9. New type of treatment facilities that do not require a sewage system.

Autonomous treatment facilities can be used in regions of the world with an underdeveloped communal infrastructure. Now engineers and scientists are working to reduce the cost of these technologies.

 
10. Digital intelligent assistants.

 

The development of artificial intelligence technologies will significantly expand the range of tasks that digital assistants can solve. For example, voice assistants are beginning to better recognize natural speech, which will make it possible to use their automation for a number of daily tasks, such as searching for information and making purchases.

FaceApp Aging Challenge: Is FaceApp A Danger To Our Privacy?

FaceApp Aging Challenge: Is FaceApp A Danger To Our Privacy?

DOES FACEAPP PROVIDE THE SPECIAL SERVICES TO THE RUSSIAN FEDERATION BY COLLECTING SELFIES AND WHAT TO DO IF THE AGING APPLICATION IS ALREADY ON OUR PHONE ?

No, FaceApp isn’t taking photographs of our face and taking them back to Russian servers  for some immoral project. At least that’s what current evidence suggests.

Even If you are more curious then you should know that users can request the removal of their data in FaceApp – it works so far.

Social networks are overwhelmed with photos in which our friends suddenly become 80 years old also stars, politicians, and characters of serials. Although FaceApp, appeared in 2017, but with the AI based algorithm it is now on an unprecedented wave of popularity.

It is a free application that will show you from an unexpected side. Every person is curious to try on different images. How will I look in old age? And if I change the skin color? Visual answers to all these questions can be obtained using the free utility FaceApp.

This App is a new utility for Android and iOS that performs interesting manipulations with portraits of people. With its help, you can easily turn a bore into a fun-loving boy, a young man into an old man, and a man into a woman. Or vice versa.

More recently, in the next update, the ability to create GIF images was added. These are small animations that you can then upload to social networks

 After Faceapp’s exponential popularity in few days, the debate about its privacy policy popped up. The main causes of concern are: it is not very clear what data it collects and what can happen to them, as well as the Russian origin of the application for “aging.” FBI is figuring out whether app is really dangerous and whether there is reason for panic.

Currently 1.9mil android users have downloaded it and because of FaceApp Challenge, in which celebs especially have been sharing images to their social media and showing their fans about their older looks in different poses.

faceapp challenge concern faceapp challenge faceapp privacy concern

 

Comprehensive and Permanent Rights to Use Your Selfies

The application works on the basis of neural networks and is developed by the Russian company Wireless Lab, which is headed by the Russian Yaroslav Goncharov – he previously worked at Yandex.

In accordance with the FaceApp terms of use, people own their own “user content”. But the company has an unrestricted license to use its content as it is free to users.

You provide FaceApp with continuous, irreversible, non-exclusive, free, comprehensive, fully rewarded rights, such that they are subject to transfer and transferred rights to use, reproduce, change, adapt, publish, translate, create derivative products, distribute, publicly execute and display your User Content and any user names, nicknames or similarities provided in connection with your User Content in all media formats and channels that are now known or will be released later and without compensation to you. When you publish or otherwise distribute User Content through our services, you understand that your User Content and any information associated with it (for example, your username, location or profile photo) will be visible to the public.

FaceApp Terms of Use

Why so much noise on Faceapp using Cyber Security umbrella?

Since its launch in 2017, the app has downloaded 86 million users worldwide through the Apple App Store and Google Play. Over the last week, on July 20, more than 15.7 million new users appeared in FaceApp. The popularity of the application these days was higher than all the time from the beginning of the year together. It was the vitality of the program for “aging” that caused such close attention – and as it turned out, the program is not so simple.

It is not known for certain whether FaceApp downloads photos in the background. While no evidence of this has been found by security experts, the creators of the application also deny this. So much attention is riveted on this, because in the gallery there can be not only photos of morning coffee, but also screenshots of bank accounts and other documents.

In addition, the Russian startup FaceApp uploads photos of users to the cloud storage, without warning about it. That is, people generally do not understand that photo processing is not on the phone.

FaceApp also indicates that it can save photos to the store within 48 hours. It is argued that this is done for the application speed and traffic optimization: for example, to make sure that the user does not upload the same photo twice.

But only something is uploaded to the cloud storage — you lose control of it.

Why do you drain fast the battery of your smartphone ?

Another cause for concern: the application gets access to the photos, even if access to them is denied on the phone – this applies to iPhones. “This is not a conspiracy, but Apple could come up with a better way to describe the resolution,”

Most importantly: over the past few years, thanks to a variety of viral Facebook and scandal applications with Cambridge Analytica, it is well known that the data are not always used with the expected purpose and are not always stored properly, reliably, safely and privately.

The application is already on the phone: should we panic?

Panic is growing already. One example: the head of security of the National Committee of the Democratic Party of the United States urged presidential candidates not to install the application, and if they did, remove it. However, there is hardly any point in removing the application. In addition, the FBI has already called to check FaceApp .

“You may be on some billboard somewhere in Moscow, but your face is most likely being used to work out some kind of artificial intelligence algorithm for recognizing faces,” said Peter Kastadinov from the Phone Arena publication.

“It is definitely strange that FaceApp saves your photos for possible” commercial use. “We assume that they can use them to teach AI new functions, but who knows: maybe you are already on a Russian photo stock!”, Writes Vox.

“However, for FaceApp, the main purpose of collecting your data is most likely advertising. There is no reason to think that the Russian authorities are doing something terrible with your photos,” adds Vox publication right away.

What is Faceapp Response on security concerns ?

The creators of FaceApp claim that they do not transfer data to third parties. According to them, all data is stored outside Russia. Also, FaceApp said that users can request the removal of their data, although there is no convenient way to do this yet. The request can be sent via the mobile application through the “settings”: there you should go to the “support”, report the error, indicating the “privacy” in the subject line.

Here’s FaceApp statement in full to privacy concerns

We are receiving a lot of inquiries regarding our privacy policy and therefore, would like to provide a few points that explain the basics:

1. FaceApp performs most of the photo processing in the cloud. We only upload a photo selected by a user for editing. We never transfer any other images from the phone to the cloud.

2. We might store an uploaded photo in the cloud. The main reason for that is performance and traffic: we want to make sure that the user doesn’t upload the photo repeatedly for every edit operation. Most images are deleted from our servers within 48 hours from the upload date.

3. We accept requests from users for removing all their data from our servers. Our support team is currently overloaded, but these requests have our priority. For the fastest processing, we recommend sending the requests from the FaceApp mobile app using “Settings->Support->Report a bug” with the word “privacy” in the subject line. We are working on the better UI for that.

4. All FaceApp features are available without logging in, and you can log in only from the settings screen. As a result, 99% of users don’t log in; therefore, we don’t have access to any data that could identify a person.

5. We don’t sell or share any user data with any third parties.

6. Even though the core R&D team is located in Russia, the user data is not transferred to Russia.

Additionally, we’d like to comment on one of the most common concerns: all pictures from the gallery are uploaded to our servers after a user grants access to the photos (for example, https://twitter.com/joshuanozzi/status/1150961777548701696). We don’t do that. We upload only a photo selected for editing. You can quickly check this with any of network sniffing tools available on the internet.

 

faceapp privacy response

It is important that all the fuss around FaceApp soon after a series of loud scandals in the field of security and technology suggests that users still do not understand that you need to think carefully before transferring your confidential data somewhere. In addition, it is not completely clear how companies collect information about people and what rights they have to it.

Therefore, one should not panic because of FaceApp alone, but it is equally necessary to treat all photo applications and programs that have access to our data with equal caution. Technology companies, of course, deserve a barrage of criticism because of their ambiguous conditions of use. Just as the users themselves for their frivolity.