Artificial Intelligence is a topic that has been getting a lot of attention, mostly because of the rapid improvement that this field has seen since the turn of the 21st century. Amazing innovations are laying the foundation for ongoing breakthrough achievements. In this article, Im going to focus on three specific topics:

In the 1950s, AI pioneers Minsky and McCarthy described artificial intelligence as any task performed by a program or a machine that, was it performed by a human, would have required that human to apply intelligence to accomplish the task.

This is a fairly broad description. Nowadays, all tasks associated with human intelligence are described as AI when performed by a computer. This includes planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, manipulation and, to a lesser extent, social intelligence and creativity.

Artificial intelligence is defined as the branch of science and technology that [is] concerned with the study of software and hardware to provide machines the ability to learn insights from data and [the] environment and the ability to adapt in changing situation[s] with high precision, accuracy and speed.Amit Ray, Compassionate Artificial Superintelligence AI 5.0AI with Blockchain, BMI, Drone, IOT and Biometric Technologies

Now that we know what AI actually means, lets find out what its used for!

While surfing the web, have you ever wondered how most ads are related to your interests? Thats a representation of AI, more specifically, machine learning. However, AI is more commonly associated with robots, such as the ability of a robot to think on its own and the potential for computer consciousness. While these would be astounding achievements, they involve highly complex algorithms which we still cant produce today.

Machine learning is a big part of AI, and it might be the key reason for this fields meteoric rise. Its based on the principle of trial and error; every time we try to solve a problem, like a maze, were going to fail at least once. However, failing is a good thing in machine learning, because it enables the program to learn new information. That information is stored as data, and each time an AI goes down a specific path, it will reference the data from prior trials to see which one will work best this time.

To expand on the above example, Im going to teach you one of the first AI algorithms (often used to solve mazes), the A* algorithm.

To understand this algorithm, lets visualize our maze as a chess board with inaccessible regions (like a maze) that well call nodes.

This is a fun example of AI in action, since flying cars would be reliant on AI to function properly. In the future, scientists believe were going to have autonomous cars that transport people to their desired destinations. This involves cars having some sort of artificial intelligence, more specifically, machine learning,because they need to always find the best possible course to the destination, not crash into buildings and respect other vehicles. A very basic implementation of this, although extremely ineffective and slow, could be the A* algorithm, where buildings represent inaccessible nodes. However, some good alternatives exist that we didnt review in detail due to their high levels of complexity:

This article was written to provide a fun introduction to AI and to show its potential for future technologies. More than ever, its crucial to know the principles of artificial intelligence since it will be so important in the future. We need to constantly be open to new ideas and approaches, such as artificial intelligence (AI), and be willing to challenge assumptions of what this technology can achieve.

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What Is Artificial Intelligence (AI)? - IoT For All

While, in the past, artificial intelligence may have been thought to be a product of science fiction, most professionals today understand that the adoption of smart technology is actively changing workplaces. There are applications of AI throughout nearly every profession and industry, and human resources careers are no exception.

A recent survey conducted by Oracle and Future Workplace found that human resources professionals believe AI can present opportunities for mastering new skills and gaining more free time, allowing HR professionals to expand their current roles in order to be more strategic within their organization.

Among HR leaders who participated in the survey, however, 81 percent said that they find it challenging to keep up with the pace of technological changes at work. As such, it is more important now than ever before for human resources professionals to understand the ways in which AI is reshaping the industry.

Read on to explore what artificial intelligence entails, how it is applied to the world of human resources management, and how HR professionals can prepare for the future of the field today.

At a high level, artificial intelligence (AI) is a technology that allows computers to learn from and make or recommend actions based on previously collected data. In terms of human resources management, artificial intelligence can be applied in many different ways to streamline processes and improve efficiency.

Uwe Hohgrawe, lead faculty for Northeasterns Master of Professional Studies in Analytics program explains that we as humans see the information in front of us and use our intelligence to draw conclusions. Machines are not intelligent, but we can make them appear intelligent by feeding them the right information and technology.

Learn More: AI & Other Trends Defining the HRM Industry

While organizations are adopting AI into their human resources processes at varying rates, it is clear to see that the technology will have a lasting impact on the field as it becomes more widely accepted. For this reason, it is important that HR professionals prepare themselves for these changes by understanding what the technology is and how it is applied across various functions.

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Among the numerous applications of AI in the human resources sector, some of the first changes HR professionals should expect to see involve recruitment and onboarding, employee experience, process improvement, and the automation of administrative tasks.

While many organizations are already beginning to integrate AI technology into their recruiting efforts, the vast majority of organizations are not. In fact, Deloittes 2019 Global Human Capital Trends survey found that only 6 percent of respondents believed that they had the best-in-class recruitment processes in technology, while 81 percent believed their organizations processes were standard or below standard. For this reason, there are tremendous opportunities for professionals to adapt their processes and reap the benefits of using this advanced technology.

During the recruitment process, AI can be used to the benefit of not only the hiring organization but its job applicants, as well. For example, AI technology can streamline application processes by designing more user-friendly forms that a job applicant is more likely to complete, effectively reducing the number of abandoned applications.

While this approach has made the role of the human resources department in recruitment much easier, artificial intelligence also allows for simpler and more meaningful applications on the candidates end, which has been shown to improve application completion rates.

Additionally, AI has played an important role in candidate rediscovery. By maintaining a database of past applicants, AI technology can analyze the existing pool of applicants and identify those that would be a good fit for new roles as they open up. Rather than expending time and resources searching for fresh talent, HR professionals can use this technology to identify qualified employees more quickly and easily than ever before.

Once hiring managers have found the best fit for their open positions, the onboarding process begins. With the help of AI, this process doesnt have to be restricted to standard business hoursa huge improvement over onboarding processes of the past.

Instead, AI technology allows new hires to utilize human resources support at any time of day and in any location through the use of chatbots and remote support applications. This change not only provides employees with the ability to go through the onboarding process at their own pace, but also reduces the administrative burden and typically results in faster integration.

In addition to improvements to the recruitment process, HR professionals can also utilize artificial intelligence to boost internal mobility and employee retention.

Through personalized feedback surveys and employee recognition systems, human resources departments can gauge employee engagement and job satisfaction more accurately today than ever before. This is incredibly beneficial considering how important it is to understand the overall needs of employees, however there are several key organizational benefits to having this information, as well.

According to a recent report from the Human Resources Professional Association, some AI software can evaluate key indicators of employee success in order to identify those that should be promoted, thus driving internal mobility. Doing so has the potential to significantly reduce talent acquisition costs and bolster employee retention rates.

This technology is not limited to identifying opportunities to promote from within, however; it can also predict who on a team is most likely to quit. Having this knowledge as soon as possible allows HR professionals to deploy retention efforts before its too late, which can strategically reduce employee attrition.

One of the key benefits of leveraging artificial intelligence in various human resources processes is actually the same as it is in other disciplines and industries: Automating low value, easily repeatable administrative tasks gives HR professionals more time to contribute to strategic planning at the organizational level. This, in turn, enables the HR department to become a strategic business partner within their organizations.

Smart technologies can automate processes such as the administration of benefits, pre-screening candidates, scheduling interviews, and more. Although each of these functions is important to the overall success of an organization, carrying out the tasks involved in such processes is generally time-consuming, and the burden of these duties often means that HR professionals have less time to contribute to serving their employees in more impactful ways.

Deploying AI software to automate administrative tasks can ease this burden. For instance, a study by Eightfold found that HR personnel who utilized AI software performed administrative tasks 19 percent more effectively than departments that do not use such technology. With the time that is saved, HR professionals can devote more energy to strategic planning at the organizational level.

While it is clear that artificial intelligence will continue to positively shape the field of human resources management in the coming years, HR professionals should also be aware of the challenges that they might face.

The most common concerns that HR leaders have focus primarily on making AI simpler and safer to use. In fact, the most common factor preventing people from using AI at work are security and privacy concerns. Additionally, 31 percent of respondents in Oracles survey expressed that they would rather interact with a human in the workplace than a machine. Moving forward, HR professionals will need to be prepared to address these concerns by staying on top of trends and technology as they evolve and change.

People will need to be aware of ethical and privacy questions when using this technology, Hohgrowe says. In human resources, [AI] can involve using sensitive information to create sensitive insights.

For instance, employees want their organizations to respect their personal data and ask for permission before using such technology to gather information about them. However organizations also want to feel protected from data breaches, and HR professionals must take the appropriate security measures into account.

To prepare for the future of human resources management, professionals should take the necessary steps to learn about current trends in the field, as well as lay a strong foundation of HR knowledge that they can build upon as the profession evolves.

Staying up to date with industry publications and networking with leaders in the field is a great way to stay abreast of current trends like the rapid adoption of artificial intelligence technologies. Building your foundational knowledge of key human resource management theories, strategy, and ethics, on the other hand, is best achieved through higher education.

Although there are many certifications and courses available that focus on specific HR topics, earning an advanced degree like a Master of Science in Human Resources Management provides students with a more holistic approach to understanding the connection between an organization and its people.

At Northeastern, we highlight the importance of three literacies: data literacy, technological literacy, and humanic literacy. That combination is one of the areas where I believe we will pave the way in the future, Hohgrawe says. This also allows us to explore augmented artificial intelligence in a way that appreciates the relationship between human, machine, and data.

Students looking to specialize in AI also have the opportunity to declare a concentration in artificial intelligence within Northeasterns human resource management program. Those who specialize in this specific aspect of the industry will study topics such as human resources information processing, advanced analytical utilization, and AI communication and visualization. Similarly, those who seek a more technical masters degree might consider a Northeasterns Master of Professional Studies in Enterprise Intelligence, which also includes a concentration in AI for human resources.

No matter each students specific path, however, those who choose to study at Northeastern will have the unique chance to learn from practitioners with advanced knowledge and experience in the field. Many of Northeasterns faculty have previously or are currently working in the human resources management field, enabling them to bring a unique perspective to the classroom and educate students on the real-world challenges that HR professionals face today.

Between the world-class faculty members and the multitude of experiential learning opportunities provided during the pursuit of a masters degree, aspiring HR professionals will graduate from Northeasterns program with the unique combination of experience and expertise needed to land a lucrative role in this growing field.

Interested in advancing your career in HR? Explore Northeasterns Master of Science in Human Resources Management program and consider taking the next step toward a career in this in-demand industry.

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Artificial Intelligence in Human Resource Management

You have probably heard that artificial intelligence could be used to do lots of impressive tasks and jobs. AI can help designers and artists make quick tweaks to visuals. AI can also help researchers identify fake images or connect touch and sense. AI is being used to program websites and apps by combining symbolic reasoning and deep learning. Basically, artificial intelligence goes beyond deep learning. Here are five reasons why AI is important to you.

It is no news that AI will replace repetitive jobs. It literally means that these kinds of jobs will be automated, like what robots are currently doing in a myriad of factories. Robots are rendering the humans that are supposed to do those tasks practically jobless.

And it goes further than that many white collar tasks in the fields of law, hospitality, marketing, healthcare, accounting, and others are adversely affected. The situation seems scary because scientists are just scratching the surface as extensive research and development of AI. AI is advancing rapidly (and it is more accessible to everybody).

Some believe that AI can create even more new jobs than ever before. According to this school of thought, AI will be the most significant job engine the world has ever seen. Artificial intelligence will eliminate low-skilled jobs and effectively create massive high-skilled job opportunities that will span all sectors of the economy.

For example, if AI becomes fully adapt to language translation, it will create a considerable demand for high-skilled human translators. If the costs of essential translations drop to nearly zero, this will encourage MORE companies that need this particular service to expand their business operations abroad.

To those who speak different languages than the community in which they reside, this help will inevitably create more work for high-skilled translators, boost more economic activities. As a result of this, and more people will be employed in these companies due to the increased workload.

Boosting international trade it one of the most significant benefits of our global times. So yes, AI will eliminate some jobs, but it will create many, many more.

AI can be used extensively in the healthcare industry. It is applicable in automated operations, predictive diagnostics, preventive interventions, precision surgery, and a host of other clinical operations. Some individuals predict that AI will completelyreshape the healthcare landscape for the better.

And here are some of the applications of artificial intelligence in healthcare:

AI is also used in the agriculture industry extensively. Robots can be used to plant seeds, fertilized crops and administer pesticides, among a lot of other uses. Farmers can use a drone to monitor the cultivation of crops and also collect data for analysis.

The value-add data will be used to increase the final output. How? The data collected is analyzed by AI on such variables as crop health and soil conditions, boosting final production, and it can also be used in harvesting, especially for crops that are difficult to gather.

AI is changing the workplace, and there are plenty of reasons to be optimistic. It is used to do lots of tedious and lengthy tasks, especially the low-skilled types of jobs that are labor-intensive. It means that employees will be retasked away from boring jobs and bring significant and positive change in the workplace.

For instance, artificial intelligence is used in the automotive industry to do repetitive tasks such as performing a routine operation in the assembly line, for example. Allowing a robot to care for well, robotic-tasks, has created a shift in the workforce.

Auto accidents are one of the most popular types of accidents that happen in America. It kills thousands of people annually. A whopping 95 percent of these accidents are caused byhuman error, meaning accidents are avoidable.

The number of accident cases will reduce as artificial intelligence is being introduced into the industry by the use of self-driving cars. On-going research in the auto industry is looking at ways AI can be used to improve traffic conditions.

Smart systems are currently in place in many cities that are used to analyze traffic lights at the intersections. Avoiding congestion leads to safer movements of vehicles, bicycles, and pedestrians.

Conclusion

Artificial intelligence is very useful in all industries as more research is being done to advance it. The advancements in this AI tech will be most useful if it is understood and trusted. An important part of it is that artificial intelligence and related technologies such as drones, robots, and autonomous vehicles can create around tens of millions of jobs over the next decade.

Having more jobs created not less will be great news for everyone. More jobs will help boost the GDP of the economy. Advancement in AI and its impressive computational power has already led to the concept of supercomputers and beyond.

Elena Randall is a Content Creator Who works for Top Software Companies, provides a top 10 list of top software development companies within the world. She is passionate about reading and writing.

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5 Reasons Why Artificial Intelligence Is Important To You

Artificial Intelligence has facilitated the processing of a large amount of data and its use in the industry. The number of tools and frameworks available to data scientists and developers has increased with the growth of AI and ML. This article on Artificial Intelligence Tools & Frameworks will list out some of these in the following sequence:

Development of neural networks is a long process which requires a lot of thought behind the architecture and a whole bunch of nuances which actually make up the system.

These nuances can easily end up getting overwhelming and not everything can be easily tracked. Hence, the need for such tools arises, where humans handle the major architectural decisions leaving other optimization tasks to such tools. Imagine an architecture with just 4 possible booleanhyperparameters, testing all possible combinations would take 4! Runs. Retraining the same architecture 24 times is definitely not the best use of time and energy.

Also, most of the newer algorithms contain a whole bunch of hyperparameters. Heres where new tools come into the picture. These tools not only help develop but also, optimize these networks.

From the dawn of mankind, we as a species have always been trying to make things to assist us in day to day tasks. From stone tools to modern day machinery, to tools for making the development of programs to assist us in day to day life. Some of the most important tools and frameworks are:

Scikit-learn is one of the most well-known ML libraries. It underpins many administered and unsupervised learning calculations. Precedents incorporate direct and calculated relapses, choice trees, bunching, k-implies, etc.

It includes a lot of calculations for regular AI and data mining assignments, including bunching, relapse and order. Indeed, even undertakings like changing information, feature determination and ensemble techniques can be executed in a couple of lines.

For a fledgeling in ML, Scikit-learn is a more-than-adequate instrument to work with, until you begin actualizing progressively complex calculations.

On the off chance that you are in the realm of Artificial Intelligence, you have most likely found out about, attempted or executed some type of profound learning calculation. Is it accurate to say that they are essential? Not constantly. Is it accurate to say that they are cool when done right? Truly!

The fascinating thing about Tensorflow is that when you compose a program in Python, you can arrange and keep running on either your CPU or GPU. So you dont need to compose at the C++ or CUDA level to keep running on GPUs.

It utilizes an arrangement of multi-layered hubs that enables you to rapidly set up, train, and send counterfeit neural systems with huge datasets. This is the thing that enables Google to recognize questions in photographs or comprehend verbally expressed words in its voice-acknowledgment application.

Theano is wonderfully folded over Keras, an abnormal state neural systems library, that runs nearly in parallel with the Theano library. Keras fundamental favorable position is that it is a moderate Python library for profound discovering that can keep running over Theano or TensorFlow.

What sets Theano separated is that it exploits the PCs GPU. This enables it to make information escalated counts up to multiple times quicker than when kept running on the CPU alone. Theanos speed makes it particularly profitable for profound learning and other computationally complex undertakings.

Caffe is a profound learning structure made with articulation, speed, and measured quality as a top priority. It is created by the Berkeley Vision and Learning Center (BVLC) and by network donors. Googles DeepDream depends on Caffe Framework. This structure is a BSD-authorized C++ library with Python Interface.

It allows for trading computation time for memory via forgetful backprop which can be very useful for recurrent nets on very long sequences.

If you like the Python-way of doing things, Keras is for you. It is a high-level library for neural networks, using TensorFlow or Theano as its backend.

The majority of practical problems are more like:

In all of these, Keras is a gem. Also, it offers an abstract structure which can be easily converted to other frameworks, if needed (for compatibility, performance or anything).

PyTorch is an AI system created by Facebook. Its code is accessible on GitHub and at the present time has more than 22k stars. It has been picking up a great deal of energy since 2017 and is in a relentless reception development.

CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK is available for anyone to try out, under an open-source license.

Out of all the tools and libraries listed above, Auto ML is probably one of the strongest and a fairly recent addition to the arsenal of tools available at the disposal of a machine learning engineer.

As described in the introduction, optimizations are of the essence in machine learning tasks. While the benefits reaped out of them are lucrative, success in determining optimal hyperparameters is no easy task. This is especially true in the black box like neural networks wherein determining things that matter becomes more and more difficult as the depth of the network increases.

Thus we enter a new realm of meta, wherein software helps up build software. AutoML is a library which is used by many Machine learning engineers to optimize their models.

Apart from the obvious time saved, this can also be extremely useful for someone who doesnt have a lot of experience in the field of machine learning and thus lacks the intuition or past experience to make certain hyperparameter changes by themselves.

Jumping from something that is completely beginner friendly to something meant for experienced developers, OpenNN offers an arsenal of advanced analytics.

It features a tool, Neural Designer for advanced analytics which provides graphs and tables to interpret data entries.

H20 is an open-source deep learning platform. It is an artificial intelligence tool which is business oriented and help them to make a decision from data and enables the user to draw insights. There are two open source versions of it: one is standard H2O and other is paid version Sparkling Water. It can be used for predictive modelling, risk and fraud analysis, insurance analytics, advertising technology, healthcare and customer intelligence.

Google ML Kit, Googles machine learning beta SDK for mobile developers, is designed to enable developers to build personalised features on Android and IOS phones.

The kit allows developers to embed machine learning technologies with app-based APIs running on the device or in the cloud. These include features such as face and text recognition, barcode scanning, image labelling and more.

Developers are also able to build their own TensorFlow Lite models in cases where the built-in APIs may not suit the use case.

With this, we have come to the end of our Artificial Intelligence Tools & Frameworks blog. These were some of the tools that serve as a platform for data scientists and engineers to solve real-life problems which will make the underlying architecture better and more robust.

You can check out theAI and Deep Learning with TensorFlow Course that is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.

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Top 12 Artificial Intelligence Tools & Frameworks | Edureka