The Future of Artificial Intelligence 2019

Artificial Intelligence (AI) and associated technologies will be present across many industries, within a considerable number of software packages, and part of our daily lives by 2020. Simpliv has also predicted that by 2020, AI will become one of the top five investment priorities for at least 30 percent of Chief Information Officers. Global software vendors are after this new gold rush. Unfortunately, though the promise of new revenue has pushed software business owners to invest in AI technologies, the truth is that most organizations do not have skilled staff to embrace AI Summit.

The trust deficit in the “capabilities of tech-enabled solutions” that exists today will vanish in the next 10 years, states In Ten Years: The Future of AI and ML. Over the next decade, we will witness a radical shift from partial mistrust and skepticism to complete dependence on AI and other advanced technologies. Most AI-powered applications are consumer facing, which is another solid reason for mainstream users to overcome the trust barrier over time. With more exposure and more access to technological solutions for their daily business, the Citizen Data Science community will pave the way for a new-technology-order world.

Artificial Intelligence is getting deeper into our daily lives, but this is not as scary as many may think. With Artificial Intelligence, we have seen things getting better and easier for us but this change does not stop at homes. Today many businesses are coming forward to use AI in new different ways so as to engage their customers, drive sales and make business processes simpler. In short, the demand for Artificial Intelligence development services will go high in the coming years.

We have already seen how Facebook was using AI to improve its ad campaigns as well as seen the effective use of AI-powered chatbots. So, what’s next? What should we expect out of Artificial Intelligence in the new year to boost business success?

The Future of AI

In the post-industrialization era, people have worked to create a machine that behaves like a human. The thinking machine is AI’s biggest gift to humankind; the grand entry of this self-propelled machine has suddenly changed the operative rules of business. In the recent years, self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories.

More Advanced AI Assistants

With the introduction of Amazon’s Alexa, Apple’s Siri, and other kinds of similar devices, consumers were seen hugely benefitting from AI assistants in their homes. Such AI assistants can be used to get a weather report, to play a song, to switch off the lights in the room, to look for some information online, and much more.

The new AI-powered technology is being embraced by the consumers. Based on the study of Adobe Analytics, it is said that around 71 percent of people who owned smart speakers used them at least once in a day while around 44 percent of smart speaker owners who admitted using it multiple times in a day. So in 2019, we will be able to see more of advanced AI assistants helping homes and workplaces as well as influencing other areas of life.

Today, users make AI assistants do basic tasks like search for information online or play a song. In the coming years, the changes will be big as AI assistants will be seen doing bigger tasks. They will be able to offer individualized experienced to the users just by recognizing the voice of the users.

So instead of just speaking to AI devices like we do today, there will soon come a time where you will be talking to your TV or refrigerator.

Job Changes

Artificial Intelligence has been instrumental in helping businesses to reach efficiency in what they do. For the same reason, job creation will be negatively affected by AI. With AI helping businesses to simplify different processes, certain positions filled up by humans will be taken off. The process of reskilling and retraining workers will become very crucial in the coming years with the increase of AI presence. The process should start as early as possible so that the people will be skilled and ready enough to take the advanced work.

Where can I learn about Artificial Intelligence for free?

There are a few places you can learn about Artificial Intelligence Summit. For example, many of the top tech universities in the world have uploaded content you can watch for free on sites like YouTube.

You could also consider taking some of their online courses on such topics.

But if you want to sit down and complete an actual course, here are some of the best courses that are available at the moment.

Artificial Intelligence Summit by Simpliv

This AI Summit will evaluate the nature and scale of changes that AI could bring about in various sectors such as retail, enterprise, consumer and commerce. It will scrutinize the opportunities and transformation that AI could bring about in development and digital platforms.

Simpliv will offer the opportunity for a vivid and thoughtful exchange of viewpoints among panel members, who will be a bright mix of:

  • Those who are pioneering path breaking innovations in the field of AI
  • Leading brands that are at the forefront of AI
  • Well-established leader
  • Tech evangelists
  • Unicorns, and
  • Keenly watched startups.

Visitors at the AI Summit will include AI managers, angel investors, decision makers, innovators, startups, designers, developers, Data Analysts, Managers and Scientists, and brand managers.

Contact Us:
Email: support@simpliv.com
Phone: 510-849-6155
Email: sudheer@simpliv.com
Phone: 9036771917
Enroll Here: https://www.simpliv.com/summit/

 

 

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Introduction of Artificial Intelligence

In today’s world, technology is growing very fast, and we are getting in touch with different new technologies day by day.

Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines.The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc.

AI is one of the fascinating and universal fields of Computer science which has a great scope in future. AI holds a tendency to cause a machine to work as a human.

 

What is Artificial Intelligence?

Introduction to AI

Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines “man-made,” and intelligence defines “thinking power”, hence AI means “a man-made thinking power.”

So, we can define AI as:

“It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions.”

Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems

With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence, and that is the awesomeness of AI.

It is believed that AI is not a new technology, and some people says that as per Greek myth, there were Mechanical men in early days which can work and behave like humans.

Why Artificial Intelligence?

Before Learning about Artificial Intelligence, we should know that what is the importance of AI and why should we learn it. Following are some main reasons to learn about AI:

  • With the help of AI, you can create such software or devices which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc.
  • With the help of AI, you can create your personal virtual Assistant, such as Cortana, Google Assistant, Siri, etc.
  • With the help of AI, you can build such Robots which can work in an environment where survival of humans can be at risk.
  • AI opens a path for other new technologies, new devices, and new Opportunities.

Goals of Artificial Intelligence

Following are the main goals of Artificial Intelligence:

  1. Replicate human intelligence
  2. Solve Knowledge-intensive tasks
  3. An intelligent connection of perception and action
  4. Building a machine which can perform tasks that requires human intelligence such as:
    • Proving a theorem
    • Playing chess
    • Plan some surgical operation
    • Driving a car in traffic
  5. Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.

What Comprises to Artificial Intelligence?

Artificial Intelligence is not just a part of computer science even it’s so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.

To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:

  • Mathematics
  • Biology
  • Psychology
  • Sociology
  • Computer Science
  • Neurons Study
  • Statistics

Introduction to AI

Advantages of Artificial Intelligence

Following are some main advantages of Artificial Intelligence:

  • High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.
  • High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
  • High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
  • Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.
  • Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
  • Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.

Disadvantages of Artificial Intelligence

Every technology has some disadvantages, and thesame goes for Artificial intelligence. Being so advantageous technology still, it has some disadvantages which we need to keep in our mind while creating an AI system. Following are the disadvantages of AI:

  • High Cost: The hardware and software requirement of AI is very costly as it requires lots of maintenance to meet current world requirements.
  • Can’t think out of the box: Even we are making smarter machines with AI, but still they cannot work out of the box, as the robot will only do that work for which they are trained, or programmed.
  • No feelings and emotions: AI machines can be an outstanding performer, but still it does not have the feeling so it cannot make any kind of emotional attachment with human, and may sometime be harmful for users if the proper care is not taken.
  • Increase dependency on machines: With the increment of technology, people are getting more dependent on devices and hence they are losing their mental capabilities.
  • No Original Creativity: As humans are so creative and can imagine some new ideas but still AI machines cannot beat this power of human intelligence and cannot be creative and imaginative.

ARTIFICIAL INTELLIGENCE SUMMIT:

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Simpliv, the learning platform, is organizing the AI Summit India on 18th November at Taj, Vivanta, Bangalore. This two-day summit will be an ideal opportunity to explore critical areas of AI such as digital transformation, AI and deep learning, AI and cybercrime, AI as a tool for enforcing accountability, the various industries that could get impacted by AI, enterprise and process automation using AI, AI and costumer experience, AI’s role in messaging in the advertising, marketing and healthcare and other sectors, AI for the Cognitive Enterprise, and much more.

Why Java is the Future of Big Data and IoT 2018

Digitization has changed the business model in companies. Today, every market analysis is dependent on data. As a result, the rate at which data is being generated is outpacing our analysis capability. Hence, big data analysis is in place with high-end analytic tools like Hadoop. Hadoop is a Java-based programming framework with high-level computational power that enables to process large data sets.

On the other hand, after the internet, the next thing that would take the world by storm may be the Internet of Things (IoT). This technology is based on artificial intelligence and embedded technology. This new wave of technology is meant to enable machines to human-like performance. However, the implementation of an embedded system needs many considerations; and here comes the role of Java in IoT.

Being in the technology space for more than 20 years as a trusted platform for development, Java has not been outdated. Furthermore, its role is ubiquitous even with the latest technology inventions.

In this blog, we will discuss the role of Java in big data and IoT and its credibility in future as well.

What does IoT do?

IoT is a means or technology to collect and manage massive amounts of data from a vast network of electronic devices and sensors, then processing the collected data, and sharing it with other connected devices or units to make real-time decisions. Basically, it creates intelligent devices. Example of such intelligent networking system is an automated security system of a house.

However, enabling the IoT would need programs which will help to easily connect it with other devices to maintain the connectivity all around the system. Here comes Java into the picture with its networking programming capability.

What does IoT do

Role of Java in IoT

Here are the features of Java which play critical roles in developing an IoT system.

Platform independence

Platform independence is an important feature when you are developing an IoT system. During the development of an embedded application, you need to consider about the below factors –

  • Processor,
  • The real-time operating system,
  • Different protocols which will be used to connect the devices.

Java ME abstracts all of the above factors. Hence, the developed IoT application can run across the many different devices without changing the code of those applications. It helps to implement write once and prototype anywhere (on different types of hardware platform) facility. As IoT mainly handles embedded system, the developers need to use the software on different chipsets or operating systems as per the requirements.

Portability

Portability over the network is one of the primary reasons for choosing Java for IoT development for almost all devices from desktop computer to mobile use Java. Also with its networking capability, it is an integral part of the internet that makes it a good fit for IoT.

Easy accessibility with the best functionalities

A developer can easily learn Java, and with its best level of object-oriented features, it provides the best level of services in an application. For example, security and scalability are two important parameters in the industry while dealing with IoT devices and Java meets that requirement. With its huge ecosystem in the place, Java makes itself more suitable for the IoT. Hence, developers with advanced Java knowledge are working on innovative IoT solutions to create a connected digital world.

Easy accessibility with the best functionalities.jpg

Extensive APIs

Java offers its users the advantage of using an extensive list of APIs which they need to apply rather than rewriting during the making of an embedded application. It makes Java a perfect choice for IoT programmers.

Flexible and easy to migrate

One of the primary reasons IoT programmers incline towards Java is because of its flexibility and virtual availability everywhere. Hence, they can do anything with Java. Additionally, the migration capability of any Java application is high. The reason behind it is if an application is developed using Java, there will not be many issues during the migration to a new platform, and the overall process will be less prone to error.

What are the benefits of using Java for IoT

When we embed Java for IoT, as a user we receive numerous benefits which ultimately reap in the business along with technical enhancement.

Here are some of the benefits mentioned below –

  • Higher Resource Availability – Being in technology space over long period Java has built up a strong community that consists of millions of developers around the world. It is a diverse ecosystem, and with a strong community back up, it is easier for a developer to learn Java easily. Hence, it helps to meet the goal of achieving a connected system.
  • Enhanced Device Performance – In IoT mainly Java Embedded is used which helps in more enhanced information exchange among devices on a timely basis which makes devices more integrated.
  • Enhanced Product Life-cycle due to High Adaptability – With Java, a product gets the ability to upgrade itself according to the business requirements and changes coming up in the market. Moreover, it manages itself with the changes without any glitch. Hence, the overall product life cycle enhances with the use of Java.
  • Increased Marketability – Since the product lifecycle gets increased and reusability of the modules, the overall market credibility of the product increases automatically.
  • Reduced Support Cost – As Java Embedded provides the ability to auto update and managing a product, the support cost gets reduced significantly.
  • Secure and Reliable – With enhanced security feature of Java, any IoT device will get security and reliability assurance over the internet.

What is the Role of Java in Big Data?

When we talk about Big data, the first thing comes in our mind is what does it actually do? Well, big data deals with enormous data set, either formatted or unformatted and process them to provide a valid output to the businesses in the required format. Here are few main purposes of big data-

  • To process a huge set of data to get insights of a trend
  • To use processed data for machine learning purpose to create automated process or system
  • Using big  data for complex pattern analysis

For the functionalities as mentioned earlier, mainly tools are used. Some of the popular tools are Apache Hadoop, Apache Spark, Apache Storm and many more. Most of these tools are Java-based, and Java concepts are widely used for data processing.

Big Data and Internet of Things are Interrelated

As IoT continues to grow, it has become one of the key sources of an infrequent amount of data. The data may be sourced from hundreds to thousands or even larger number of IoT devices as random data. This huge set of data also needs analysis through big data. Thus there is an interdependency of both the technologies where Java works as a common platform.

Big Data and Internet of Things are Interrelated.jpg

What will be the Role of Java in Big Data and IoT in Future?

Internet of Things is triggering millions of devices to connect online which is resulting in data more than ever. This huge data needs enough storage and management. For this purpose, big data technologies must be augmented to handle this data effectively. Interestingly the technology giants like Google and Apache are contributing more libraries for these technologies advancement. As we have discussed the role of Java in big data and IoT, it is expected that Java development will play the more aggressive role for the future benefit of these technologies.

 Overall, Java has always been considered as a popular and useful technology which is also a trusted platform when compared to all the others programming languages on the market. Though there are numerous programming languages are in place with easier interfaces like Pig, Ruby and many more; still, people show their gravity towards Java. As a result, the numbers of Java programmers are increasing every day.

Thus, whether or not the technologies like big data and IoT change rapidly, the role of Java in Big data and IoT will always remain the same.

Conclusion: To conclude, the bottom line is – Java is everywhere. However, if you want to walk with the changing industry trends, then Java is not the ultimate answer for achieving a promising career. You need to ramp up with latest technologies like Big data, Machine learning, IoT, Cloud or similar technologies. However, an effective upgradation needs proper guidance and roadmaps and here comes the role of Whizlabs to help you out in your path of success.

Do you want free online courses Click here

What is AI?

Although there is often lots of hype surrounding Artificial Intelligence (AI), once we strip away the marketing fluff, what is revealed is a rapidly developing technology that is already changing our lives. But to fully appreciate its potential, we need to understand what it is and what it is not!

Defining “intelligence” is tricky, but key attributes include logic, reasoning, conceptualization, self-awareness, learning, emotional knowledge, planning, creativity, abstract thinking, and problem solving. From here we move onto the ideas of self, of sentience, and of being. Artificial Intelligence is therefore a machine which possesses one or many of these characteristics.

However, no matter how you define it, one of AI’s central aspects learning. For a machine to demonstrate any kind of intelligence it must be able to learn.

When most technology companies talk about AI, they are in fact talking about Machine Learning (ML) — the ability for machines to learn from past experiences to change the outcome of future decisions. Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed.”

The science of getting computers to act without being explicitly programmed

In this context, past experiences are datasets of existing examples which can be used as training platforms. These datasets are varied and can be large, depending on the area of application. For example, a machine learning algorithm can be fed a large set of images about dogs, with the goal of teaching the machine to recognize different dog breeds.

Likewise, future decisions, refers to the the answer given by the machine when presented with data which it hasn’t previously encountered, but is of the same type as the training set. Using our dog breed example, the machine is presented with a previously unseen image of a Spaniel and the algorithm correctly identifies the dog as a Spaniel.

Training vs Inference

Machine Learning has two distinct phases: training and inference. Training generally takes a long time and can be resource heavy. Performing inference on new data is comparatively easy and is the essential technology behind computer vision, voice recognition, and language processing tasks.

Deep Neural Networks (DNNs), also known as deep learning, are the most popular techniques used for Machine Learning today.

Neural Networks

Traditionally, computer programs are built using logical statements which test conditions (if, and, or, etc). But a DNN is different. It is built by training a network of neurons with data alone.

DNN design is complicated, but put simply, there are a set of weights (numbers) between the neurons in the network. Before the training process begins, weights are generally set to random small numbers. During training, the DNN will be shown many examples of inputs and outputs, and each example will help refine the weights to more precise values. The final weights represents what has really been learned by the DNN.

As a result you can then use the network to predict output data given input data with a certain degree of confidence.

Once a network is trained, it is basically a set of nodes, connections, and weights. At this point it is now a static model, one that can be used anywhere needed.

To perform inference on the now static model, you need lots of matrix multiplications and dot product operations. Since these are fundamental mathematical operations, they can be run on a CPU, GPU, or DSP, although the power efficiency may vary.

Cloud

Today, the majority of DNN training and inference happens in the cloud. For example, when you use voice recognition on your smartphone, your voice is recorded by the device and sent up to the cloud for processing on a Machine Learning server. Once the inference processing has occurred, a result is sent back to the smartphone.

The advantage of using the cloud is that the service provider can more easily update the neural network with better models; and deep, complex models can be run on dedicated hardware with less severe power and thermal constraints.

However there are several disadvantages to this approach including time lag, risk of privacy, reliability, and providing enough servers to meet demand.

On-device inference

There are arguments for running inference locally, say on a smartphone, rather than in the cloud. First of all it saves network bandwidth. As these technologies become more ubiquitous there will be a sharp spike in data sent back and forth to the cloud for AI tasks.

Second, it saves power — both on the phone and in the server room — since the phone is no longer using its mobile radios (Wi-Fi or 4G/5G) to send or receive data and a server isn’t being used to do the processing.

Inference done locally delivers quicker results

There is also the issue of latency. If the inference is done locally, then the results will be delivered quicker. Plus there are myriad privacy and security advantages to not having to send personal data up to the cloud.

While the cloud model has allowed ML to enter into the mainstream, the real power of ML will come from the distributed intelligence gained when local devices can work together with cloud servers.

Heterogeneous computing

Since DNN inference can be run on different types of processors (CPU, GPU, DSP, etc.), it is ideal for true heterogeneous computing. The fundamental element of heterogeneous computing is the idea that tasks can be performed on different types of hardware, and yield different performance and power efficiency.

For example, Qualcomm offers an Artificial Intelligent Engine (AI Engine) for its top- and mid-tier processors. The hardware, combined with the Qualcomm Neural Processing SDK and other software tools, can run different types of DNNs, in a heterogeneous manner. When presented with a Neural Network built using 8-bit integers (known as INT8 networks), the AI Engine can run that on either the CPU or for better energy efficiency on the DSP. However, if the model uses 16-bit and 32-bit floating point numbers (FP16 & FP32), then the GPU would be a better fit.

The possibilities for AI augmented smartphone experiences are limitless

The software side of the AI Engine is agnostic in that Qualcomm’s tools support all the popular frameworks like Tensorflow and Caffe2, interchange formats like ONNX, as well as Android Oreo’s built-in Neural Network API. On top of that there is a specialized library for running DNNs on the Hexagon DSP. This library takes advantage of the Hexagon Vector eXtensions (HVX) that exist in top and mid-tier Snapdragon processors.

The possibilities for smartphone and smart-home experiences augmented by AI are almost limitless. Improved visual intelligence, improved audio intelligence, and maybe most importantly, improved privacy since all this visual and audio data remains local.

But AI assistance isn’t just for smartphone and IoT devices. Some of the most interesting advances are in the auto industry. AI is revolutionizing the future of the car. The long- term goal is to offer high levels of autonomy, however that isn’t the only goal. Driver assistance and driver awareness monitoring are some of the fundamental steps towards full autonomy that will drastically increase safety on our roads. Plus, with the advent of better natural user interfaces the overall driving experience will be redefined.

Wrap-up

Regardless of how it is marketed, Artificial Intelligence is redefining our mobile computing experiences, our homes, our cities, our cars, the healthcare industry — just about everything you can think of. The ability for devices to perceive (visually and audibly), infer context, and anticipate our needs allows product creators to offer new and advanced capabilities.

Machine Learning is redefining our mobile computing experiences

With more of these capabilities running locally, rather than in the cloud, the next generation of AI augmented products will offer better response times and more reliability, while protecting our privacy.

 

 

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Top 10 Technology Trends of 2018

2017 became the Year of Intelligence: the advance of technological achievements has triggered exciting and unexpected trends with wider impact horizons and very promising business prospects. This year we expect drastic exponential changes in every technological direction. Machine learning and artificial intelligence will transform the entire industries, making way for virtual helpers and a myriad of cases for automatization. The Internet of Things (IoT) will become more intelligent, uncovering a huge potential for smart homes and smart cities. A more efficient human-machine interaction will become established with the natural language replacing specific commands.

In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.

1. Artificial intelligence will reshape the business strategies

AI brings enormous changes to business operations, reshaping entire industries with the power of advanced technologies and software. Some companies now acknowledge the value of implementing the AI strategies for their business, and a major leap towards AI is on the way. Large companies with over 100,000 employees are more likely to implement the AI strategies, but for them, this process can be especially challenging. 2018 will be the year when the leading firms will incorporate AI applications into their strategic and organizational development. Additionally, there is a potential for algorithms marketplaces, where the best solutions created by engineers or companies can be shared, bought, and deployed for organizations’ individual use.

Brave ideas that used to be hard to believe, are becoming real. The constant development of machine learning and AI technologies will make every business become data-driven, and every industry smarter. After years of background work on prototypes and ideas, the new solutions will be breathtaking. Virtual assistance for patients, computational drug discovery, and genetics research give a glimpse of the amazing use cases in medicine. Many more applications for automation, robotization, and data management in different industries will bring significant changes. Healthcare, construction, banking, finance, manufacturing — every existing industry will be reshaped.

Top 10 Technology Trends of 2018a

2. Blockchain will reveal new opportunities in different industries

Everyone is now talking about blockchain, a revolutionary decentralized technology that stores and exchanges data for cryptocurrencies. It forms a distributed database with a digital register of the transactions and contracts. Blockchain stores an ever-growing list of ordered records called blocks, each containing a timestamp and a link to the previous block. Blockchain has impressive prospects in the field of digital transactions which will open new business opportunities in 2018.

This technology also uncovers many new possibilities with various applications in various other fields. Due to the growing role of social responsibility and security on the internet, the blockchain technologies are becoming increasingly relevant. In a system using blockchain, it is nearly impossible to forge any digital transactions, so the credibility of such systems will surely strengthen. This approach can become fundamental for disruptive digital business in enterprises and startups. Companies, previously operating offline, will be able to translate the processes into the digital environment completely.

Business needs to account for the blockchain risks and opportunities and analyze how this technology can influence the customer behavior. As the initial hype around blockchain in the financial services’ industry will slow down, we will see many more potential use cases for the government, healthcare, manufacturing, and other industries. For example, blockchain strongly influences the intellectual property management and opens new insights in protection from copyright infringement. Some websites like Blockai, Pixsy, Mediachain, and Proof of Existence intend to apply the blockchain technology for this purpose.

3. New approaches to privacy and security are coming

The technological development boosts the importance of data, so hacking techniques become ever more progressive. The increase in numbers of devices connected to the internet creates more data but also makes it more vulnerable and less protected. IoT gadgets are getting more popular and widely used, yet they remain extremely insecure in terms of the data privacy. Any large enterprises are constantly under threat of hack attacks, as it happened with Uber and Verizon in 2017.

Luckily, the solutions are achievable, and this year we will see great improvements in the data protection services. Machine learning will be the most significant security trend establishing a probabilistic, predictive approach to ensuring data security. Implementing techniques like behavioral analysis enables detecting and stopping an attack capable of bypassing the static protective systems. Blockchain brought our attention to a new technology called Zero Knowledge Proof which will further develop in 2018 enabling transactions that secure users’ privacy using mathematics. Another new approach to safety is known as CARTA (Continuous adaptive risk and trust assessment). It is based on a continuous evaluation of the potential risks and the degree of trust, adapting to every situation. This applies to all business participants: from the company’s developers to partners. Although our security is still vulnerable, there are promising solutions that can bring better privacy into our lives.

4. The Internet of Things will become intelligent

The intelligent things are everyday devices capable of smarter interactions with people and the environment. These things operate either semi-autonomously or autonomously in uncontrolled real-world conditions without the need for human intervention.

Intelligent things have been in a spotlight for several years, and with a continuous expansion and enhancement in 2018 they will influence another global trend — the Internet of Things.

A network of collaborative intelligent things will be created where multiple devices will work together developing IoT to its full potential. Connected to the global web and combined via wired and wireless communication channels, things will turn into a one big integrated system driving a major shift in the human-machine interaction. The fusion of artificial intelligence with the Internet of things brings about new amazing technologies to create smart homes and cities.

5. Deep learning will be faster and data collection better

Nowadays, deep learning faces certain challenges associated with the data collection and the complexity of the computations. Innovations in hardware are now being developed to speed up the deep learning experiments, e.g. the new GPUs with a greater number of cores and a different form of architecture. According to Marc Edgar, a Senior Information Scientist at the GE Research, deep training will shorten the development time of software solutions from several months to several days within the next 3-5 years. This will improve the functional characteristics, increase productivity, and reduce product costs.

Currently, most large firms realize the importance of data collection and its influence on the business effectiveness. In the coming year, companies will start using even more data, and the success will depend on the ability to combine the disparate data. In 2018, companies will collect customer data via CRM, ticket systems, BMP and DMP, as well as the omnichannel platforms. The popularity of collecting data on specialized sensors like LIDAR is also on the rise. Integrating the existing systems with all types of client data into a single information pool will definitely be on trend. Startups will continue to create new methods for gathering and using data, further reducing the costs.

6. AI will refine auto constructing and tuning of models

Top 10 Technology Trends of 2018b

Since Google’s launch of AutoML last year, use of the AI tools to accelerate the process of constructing and tuning models is rapidly gaining popularity. This new approach to AI development allows automating the design of machine learning models and enables the construction of models without human input with one AI becoming the architect of another.

This year, experts expect growth in popularity of the commercial AutoML packages and integration of AutoML into large machine learning platforms.

After AutoML, a computer vision algorithm called NASNet was built to recognize objects in video streams in real time. The “reinforcement learning” on NASNet implemented with AutoML can train the model without humans showing better results when compared to the algorithms that require human input.

These developments significantly broaden the horizons for machine learning and will completely reshape the approach to model construction in the next years.

7. The CDO role will grow extensively

The Chief Data Officers (CDOs) and other senior data professionals are getting more involved in the top management of large organizations changing their approach to data management. CDOs are the driving force behind the innovation and differentiation: they revolutionize the existing business models, improve the corporate communication with the target audience, and explore new opportunities to improve the business performance. Although this position is quite new, it is getting mainstream. According to Gartner, by 2019 CDO positions will be present in 90% of large organizations, but only half of them will actually succeed. Strong personal qualities, understanding of the responsibilities and potential obstacles are considered crucial to achieving success, yet there is another important step to unlock the full CDO’s potential. Firms should consider branching the IT department into the “I” and “T” separately, and CDOs should take the lead in the new group responsible for the information management.

8. The debates on ethics will flare up

As the AI industry makes significant progress in performing various tasks and actions in the everyday life, questions are raised regarding ethics, responsibilities, and human engagement. Who will be to blame if an artificial intelligence unit performs an illegal act? Do AI bots need any regulations? Will they be able to take over all the human jobs?

The first two questions assume that one day a bot will be legally recognized as a person and could take responsibility or be punished for their actions. Although this perspective is still years away, the debates around ethics are heating up already. Considering different possibilities, scientists are trying to find a compromise regarding the bots’ rights and responsibilities.

However, the possibility that robots will take all the workplaces is actually close to zero. Of course, the AI industry is developing extremely fast, but it is still pretty much in its infancy. 2018 promises to take the hype around this question down. Once we dive deeper into this subject, understand how to interact with the AI, and get used to it, the myth about robots taking over will surely be dispelled.

9. No more specific commands: growing of NLP

Use of chatbots in customer service became one of the leading trends of the outgoing year. In 2018 applications will require the ability to recognize the little nuances of our speech. The users want to get a response from their software by asking questions and giving commands in their natural language without thinking about the “right” way to ask. The development of NLP and its integration into computer programs will be one of the most exciting challenges of the 2018 year. We have high expectations about this.

What seems as a simple task for a human — to understand the tone of speech, the emotional coloring, and the double meaning — can be a difficult task for a computer accustomed to understanding the language of specific commands. These complex algorithms require many steps of predictions and computations, all occurring in the cloud within a split-second. With the help of NLP, people will be able to ask more questions, receive apposite answers and obtain better insights on their problems.

Top 10 Technology Trends of 2018c

10. Self-teaching AI will be more confident without the human data

Since the invention of the first artificial intelligence, the future in this field approaches faster than we expect. Experts were predicting that the AI would beat humans in the Go game by 2027. But it happened 10 years earlier — in 2017. It took only 40 days for the algorithm AlphaGo Zero to become the best Go player in the history of mankind. It was teaching itself without the input of any human data and developed strategies impossible for human players.

Next year the race for the creation of a developed, self-taught artificial intelligence will only continue. We look forward to the AI breakthrough in solving many human routines: decision-making, developing businesses and scientific models, recognition of objects, emotions, and speeches, and reinventing the customer experience. Also, we expect that AI will be able to cope with these tasks better, faster, and cheaper than people. The capability of algorithms for self-learning brings us closer to implementing the AI into many areas of human life.

Conclusions

To conclude, the year 2018 will bring great progress in technological innovations. We will witness faster and more accurate Machine Learning and AI applications and some new exciting developments. The exponential improvement of technologies like the Internet of Things, NLP, and self-teaching AI will change every business industry and our everyday lives. Although this can create a certain threat to the data security, the new approaches and solutions are continuously evolving. The changes will be streamlined and the outcomes are sure to be amazing.

The above list of trends is not definitive, please share your ideas about the main technology tendencies for this year in the comment section below. More

 

Best Machine Learning, Deep Learning, AI & IOS Courses Online

Statistics and Data Science in R

Best Machine Learning, Deep Learning, AI & IOS Courses Online2

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.

Let’s parse that.

  • Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.
  • Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
  • Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.
    What you will learn
    • Harness R and R packages to read, process and visualize data
    • Understand linear regression and use it confidently to build models
    • Understand the intricacies of all the different data structures in R
    • Use Linear regression in R to overcome the difficulties of LINEST() in Excel
    • Draw inferences from data and support them using tests of significance
    • Use descriptive statistics to perform a quick study of some data and present results

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Complete iOS 11 Machine Learning Masterclass

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If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging.

In this course, you will:

  • Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
  • Develop an intuitive sense for using Machine Learning in your iOS apps
  • Create 7 projects from scratch in practical code-along tutorials
  • Find pre-trained ML models and make them ready to use in your iOS apps
  • Create your own custom models
  • Add Image Recognition capability to your apps
  • Integrate Live Video Camera Stream Object Recognition to your apps
  • Add Siri Voice speaking feature to your apps
  • Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
  • Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
  • Get FREE unlimited hosting for one year
  • And more!

 

What you will learn
  • Build smart iOS 11 & Swift 4 apps using Machine Learning
  • Use trained ML models in your apps
  • Convert ML models to iOS ready models
  • Create your own ML models
  • Apply Object Prediction on pictures, videos, speech and text
  • Discover when and how to apply a smart sense to your apps

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Introduction to Data Science with Python

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This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

What you will learn
  • Writing simple Python scripts to do basic mathematical and logical operations
  • Loading structured data in a Python environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

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Introduction to Data Science with R

 

Data Science with Rgw.png

This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

What you will learn
  • Writing simple R programs to do basic mathematical and logical operations
  • Loading structured data in a R environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

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Machine Learning In The Cloud With Azure Machine Learning

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The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies – both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines – we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data.

This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis.

You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example

Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not – based on their credit history, historical loan applications, customers’ data and so on

Or the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior.

 

What you will learn
  • Learn about Azure Machine Learning
  • Learn about various machine learning algorithms supported by Azure Machine Learning
  • Learn how to build and run a machine learning experiment with real world datasets
  • Learn how to use classification machine learning algorithms
  • Learn how to use regression machine learning algorithms
  • Learn how to expose the Azure ML machine learning experiment as a web service or API
  • Learn how to integrate the Azure ML machine learning experiment API with a web application

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