Monthly Archives: March 2020

Artificial intelligence is also being used to analyse vast amounts of molecular information looking for potential new drug candidates a process that would take humans too long to be worth doing. Indeed, machine learning could soon be indispensable to healthcare.

Artificial intelligence can also help us manage highly complex systems such as global shipping networks. For example, the system at the heart of the Port Botany container terminal in Sydney manages the movement of thousands of shipping containers in and out of the port, controlling a fleet of automated, driverless straddle-carriers in a completely human-free zone. Similarly, in the mining industry, optimisation engines are increasingly being used to plan and coordinate the movement of a resource, such as iron ore, from initial transport on huge driverless mine trucks, to the freight trains that take the ore to port.

AIs are at work wherever you look, in industries from finance to transportation, monitoring the share market for suspicious trading activity or assisting with ground and air traffic control. They even help to keep spam out of your inbox. And this is just the beginning for artificial intelligence. As the technology advances, so too does the number of applications.

SO WHATS THE PROBLEM?

Rather than worrying about a future AI takeover, the real risk is that we can put too much trust in the smart systems we are building. Recall that machine learning works by training software to spot patterns in data. Once trained, it is then put to work analysing fresh, unseen data. But when the computer spits out an answer, we are typically unable to see how it got there.

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The real risks of artificial intelligence - BBC Future

Artificial intelligence, or AI, is a computer system which learns from the experiences it encounters. It can adjust on its own to new inputs, allowing it to perform tasks in a way that is similar to what a human would do. How we have defined AI over the years has changed, as have the tasks weve had these machines complete.

As a term, artificial intelligence was defined in 1956. With increasing levels of data being processed, improved storage capabilities, and the development of advanced algorithms, AI can now mimic human reasoning. AI personal assistants, like Siri or Alexa, have been around for military purposes since 2003.

With these artificial intelligence pros and cons, it is important to think of this technology as a decision support system. It is not the type of AI from science-fiction stories which attempts to rule the world by dominating the human race.

1. Artificial intelligence completes routine tasks with ease.Many of the tasks that we complete every day are repetitive. That repetition helps us to get into a routine and positive work flow. It also takes up a lot of our time. With AI, the repetitive tasks can be automated, finely tuning the equipment to work for extended time periods to complete the work. That allows human workers to focus on the more creative elements of their job responsibilities.

2. Artificial intelligence can work indefinitely.Human workers are typically good for 8-10 hours of production every day. Artificial intelligence can continue operating for an indefinite time period. As long as there is a power resource available to it, and the equipment is properly cared for, AI machines do not experience the same dips in productivity that human workers experience when they get tired at the end of the day.

3. Artificial intelligence makes fewer errors.AI is important within certain fields and industries where accuracy or precision is the top priority. When there are no margins for error, these machines are able to breakdown complicated math constructs into practical actions faster, and with more accuracy, when compared to human workers.

4. Artificial intelligence helps us to explore.There are many places in our universe where it would be unsafe, if not impossible, for humans to see. AI makes it possible for us to learn more about these places, which furthers our species knowledge database. We can explore the deepest parts of the ocean because of AI. We can journey to inhospitable planets because of AI. We can even find new resources to consume because of this technology.

5. Artificial intelligence can be used by anyone.There are multiple ways that the average person can embrace the benefits of AI every day. With smart homes powered by AI, thermostat and energy regulation helps to cut the monthly utility bill. Augmented reality allows consumers to picture items in their own home without purchasing them first. When it is correctly applied, our perception of reality is enhanced, which creates a positive personal experience.

6. Artificial intelligence makes us become more productive.AI creates a new standard for productivity. It will also make each one of us more productive as well. If you are texting someone or using word processing software to write a report and a misspelled word is automatically corrected, then youve just experienced a time benefit because of AI. An artificial intelligence can sift through petabytes of information, which is something the human brain is just not designed to do.

7. Artificial intelligence could make us healthier.Every industry benefits from the presence and use of AI. We can use AI to establish healthier eating habits or to get more exercise. It can be used to diagnose certain diseases or recommends a treatment plan for something already diagnosed. In the future, AI might even assist physicians who are conducting a surgical procedure.

8. Artificial intelligence extends the human experience.With an AI helping each of us, we have the power to do more, be more, and explore more than ever before. In some ways, this evolutionary process could be our destiny. Some believe that computers and humanity are not separate, but instead a single, cognitive unit that already works together for the betterment of all. Through AI, people who are blind can now see. Those who are deaf can now hear. We become better because we have a greater capacity to do thins.

1. Artificial intelligence comes with a steep price tag.A new artificial intelligence is costly to build. Although the price is coming down, individual developments can still be as high as $300,000 for a basic AI. For small businesses operating on tight margins or low initial capital, it may be difficult to find the cash necessary to take advantage of the benefits which AI can bring. For larger companies, the cost of AI may be much higher, depending upon the scope of the project.

2. Artificial intelligence will reduce employment opportunities.There will be jobs gained because of AI. There will also be jobs lost because of it. Any job which features repetitive tasks as part of its duties is at-risk of being replaced by an artificial intelligence in the future. In 2017, Gartner predicted that 500,000 net jobs would be created because of AI. On the other end of the spectrum, up to 900,000 jobs could be lost because of it. Those figures are for jobs only within the United States.

3. Artificial intelligence will be tasked with its own decisions.One of the greatest threats we face with AI is its decision-making mechanism. An AI is only as intelligent and insightful as the individuals responsible for its initial programming. That means there could be a certain bias found within is mechanisms when it is time to make an important decision. In 2014, an active shooter situation caused people to call Uber to escape the area. Instead of recognizing the dangerous situation, the algorithm Uber used saw a spike in demand, so it decided to increase prices.

4. Artificial intelligence lacks creativity.We can program robots to perform creative tasks. Where we stall out in the evolution of AI is creating an intelligence which can be originally creative on its own. Our current AI matches the creativity of its creator. Because there is a lack of creativity, there tends to be a lack of empathy as well. That means the decision of an AI is based on what the best possible analytical solution happens to be, which may not always be the correct decision to make.

5. Artificial intelligence can lack improvement.An artificial intelligence may be able to change how it reacts in certain situations, much like a child stops touching a hot stove after being burned by it. What it does not do is alter its perceptions, responses, or reactions when there is a changing environment. There is an inability to distinguish specific bits of information observed beyond the data generated by that direct observation.

6. Artificial intelligence can be inaccurate.Machine translations have become an important tool in our quest to communicate with one another universally. The only problem with these translations is that they must be reviewed by humans because the words, not the intent of the words, is what machines translate. Without a review by a trained human translator, the information received from a machine translation may be inaccurate or insensitive, creating more problems instead of fewer with our overall communication.

7. Artificial intelligence changes the power structure of societies.Because AI offers the potential to change industries and the way we live in numerous ways, societies experience a power shift when it becomes the dominant force. Those who can create or control this technology are the ones who will be able to steer society toward their personal vision of how people should be. It also removes the humanity out of certain decisions, like the idea of having autonomous AI responsible for warfare without humans actually initiating the act of violence.

8. Artificial intelligence treats humanity as a commodity.When we look at the possible outcomes of AI on todays world, the debate is often about how many people benefit compared to how many people will not. The danger here is that people are treated as a commodity. Businesses are already doing this, looking at the commodity of automation through AI as a better investment than the commodity of human workers. If we begin to perceive ourselves as a commodity only, then AI will too, and the outcome of that decision could be unpredictable.

These artificial intelligence pros and cons show us that our world can benefit from its presence in a variety of ways. There are also many potential dangers which come with this technology. Jobs may be created, but jobs will be lost. Lives could be saved, but lives could also be lost. That is why the technologies behind AI must be made available to everyone. If only a few hold the power of AI, then the world could become a very different place in a short period of time.

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16 Artificial Intelligence Pros and Cons Vittana.org

Alan Crameri, CTO, Barrachd explains that the rise of artificial intelligence will lead to the fourth industrial revolution

'AI is a journey. And the journey to AI starts with 'the basics' of identifying and understanding the data. Where does it reside? How can we access it? We need strong information architecture as the first step on our AI ladder'.

Artificial Intelligence (AI) has been described as the fourth industrial revolution. It will transform all of our jobs and lives over the next 10 years. However, it is not a new concept. AIs roots are in the expert systems of the 70s and 80s, computers that were programmed with a humans expert knowledge in order to allow decision-making based on the available facts.

Whats different today, and is enabling this revolution, is the evolution of machine learning systems. No longer are machines just capturing explicit knowledge (where a human can explain a series of fairly logical steps). They are now developing a tacit knowledge the intuitive, know-how embedded in the human mind. The kind of knowledge thats hard to describe, let alone transfer.

Machine learning is already all around us, unlocking our phones with a glance or a touch, suggesting music we like to listen to, and teaching cars to drive themselves.

>Read more onArtificial Intelligence what CTOs and co need to know

Underpinning all this is the explosion of data. Data is growing faster than ever before. By the year 2020, its estimated that every human being on the planet will be creating 1.7 megabytes of new information every second! There will be 50 billion smart connected devices in the world, all developed to collect, analyse and share data. This data is vital to AI. Machine learning models need data Just as we humans learn our tacit knowledge through our experiences, by attempting a task again and again to gradually improve, ML models need to be trained.

AI is a journey. And the journey to AI starts with the basics of identifying and understanding the data. Where does it reside? How can we access it? We need strong information architecture as the first step on our AI ladder.

Of course, some data may be difficult it might be unstructured, it may need refinement, it could be in disparate locations and from different sources. So, the next step is to fuse together this data in order to allow analytics tools to find better insight.

The next step in the journey is identifying and understanding the patterns and trends in our data with smart analytics techniques.

>Read more onA guide to artificial intelligence in enterprise: Is it right for your business?

Only once these steps of the journey have been completed can we truly progress to AI and machine learning, to gain further insight into the past and future performance of our organisations, and to help us solve business problems more efficiently.

But once that journey is complete the architecture, the data fusion, the analytics solutions the limits of possibility are only contained by the availability of data. So lets look at some examples where were already using these techniques.

Lets take an example that is applicable to most organisations the management of people. Businesses can fuse employee and payroll data, absence records, training records, performance ratings and more to give a complete picture of an employees interaction with the organisation. Managers can instantly visualise how people are performing, and which areas to focus on for improvement. The next stage is to use AI models to predict those employees who might need some extra support or intervention high-performers at risk of leaving, or people showing early signs of declining performance.

But what about when you focus instead on the customer? Satisfaction, retention, and interaction increasingly businesses look to social media to track the sentiment and engagement of their relationships with customers and consumers. Yet finding meaningful patterns and insights amongst a continual flow of diverse data can be difficult.

Social media analytics solutions can be used to analyse how customers and consumers view and react to the companies and brands theyre interacting with through social media.

>Read more onArtificial intelligence: Transforming the insurance industry

The data is external to the organisations concerned but is interpreted to create an information architecture behind the scenes. The next stop on the AI journey enables powerful analysis of trends and consumer behaviour over time, allowing organisations to track and forecast customer engagement in real-time.

Social media data isnt the only source of real time engagement. Customer data is an increasingly rich vein that can be tapped into. Disney is already collecting location data from wristbands at their attractions, predicting and managing queue lengths (suggesting other rides with shorted queues, or offering food/drink vouchers in busy times to reduced demand). Infrared cameras are even watching people in movie theatres and monitoring eye movements and facial expressions to determine engagement and sentiment.

The ability to analyse increasingly creative and diverse data sources to unearth new insights is growing, but the ability to bring together these new, disparate data sources is key to realising their value.

There are huge opportunities around the sharing and fusion of data, in particular between different agencies (local government, health, police). But this comes with significant challenges around privacy, data protection and a growing public concern.

The next step is to predict the future when and where crime is likely to happen, or the risk or vulnerability of individuals, allowing the police to direct limited resources as efficiently as possible. Machine learning algorithms can be employed in a variety of ways to automate facial recognition, to pinpoint crime hotspots, and to identify which people are more likely to reoffend.

>Read more onArtificial intelligence: Data will be the differentiator in the marketplace

AI models are good at learning to recognise patterns. And these patterns arent just found in images, but in sound too. Models already exist that can listen to the sounds within a city, and detect the sound of a gunshot a large proportion of which go unreported. Now lamppost manufacturers are building smart street lights, which monitor light, sound, weather and other environmental variants. By introducing new AI models, could we allow them to detect gunshots at scale, helping police to respond quickly and instantly when a crime is underway?

However, there is one underlying factor that occurs across every innovative solution now, and in the future. Data quality. IBM has just launched an AI tool deigned to monitor artificial intelligence deployments, and assess accuracy, fairness and bias in the decisions that they make. In short, AI models monitoring other AI models.

Lets just hope that the data foundation that these are built on is correct at the end of the day, if the underlying data is flawed, then so will be the AI model, and so will be the AI monitoring the AI! And thats why the journey to advanced analytics, AI and machine learning is so important. Building a strong information architecture, investing in intelligent data fusion and creating a solid analytics foundation is vital to the success of future endeavours in data.

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Artificial Intelligence: The fourth industrial revolution

by Rockwell Anyoha

In the first half of the 20th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the heartless Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why cant machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.

Unfortunately, talk is cheap. What stopped Turing from getting to work right then and there? First, computers needed to fundamentally change. Before 1949 computers lacked a key prerequisite for intelligence: they couldnt store commands, only execute them. In other words, computers could be told what to do but couldnt remember what they did. Second, computing was extremely expensive. In the early 1950s, the cost of leasing a computer ran up to $200,000 a month. Only prestigious universities and big technology companies could afford to dillydally in these uncharted waters. A proof of concept as well as advocacy from high profile people were needed to persuade funding sources that machine intelligence was worth pursuing.

Five years later, the proof of concept was initialized through Allen Newell, Cliff Shaw, and Herbert Simons, Logic Theorist. The Logic Theorist was a program designed to mimic the problem solving skills of a human and was funded by Research and Development (RAND) Corporation. Its considered by many to be the first artificial intelligence program and was presented at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in 1956. In this historic conference, McCarthy, imagining a great collaborative effort, brought together top researchers from various fields for an open ended discussion on artificial intelligence, the term which he coined at the very event. Sadly, the conference fell short of McCarthys expectations; people came and went as they pleased, and there was failure to agree on standard methods for the field. Despite this, everyone whole-heartedly aligned with the sentiment that AI was achievable. The significance of this event cannot be undermined as it catalyzed the next twenty years of AI research.

From 1957 to 1974, AI flourished. Computers could store more information and became faster, cheaper, and more accessible. Machine learning algorithms also improved and people got better at knowing which algorithm to apply to their problem. Early demonstrations such as Newell and Simons General Problem Solver and Joseph Weizenbaums ELIZA showed promise toward the goals of problem solving and the interpretation of spoken language respectively. These successes, as well as the advocacy of leading researchers (namely the attendees of the DSRPAI) convinced government agencies such as the Defense Advanced Research Projects Agency(DARPA) to fund AI research at several institutions. The government was particularly interested in a machine that could transcribe and translate spoken language as well as high throughput data processing. Optimism was high and expectations were even higher. In 1970 Marvin Minsky told Life Magazine, from three to eight years we will have a machine with the general intelligence of an average human being. However, while the basic proof of principle was there, there was still a long way to go before the end goals of natural language processing, abstract thinking, and self-recognition could be achieved.

Breaching the initial fog of AI revealed a mountain of obstacles. The biggest was the lack of computational power to do anything substantial: computers simply couldnt store enough information or process it fast enough. In order to communicate, for example, one needs to know the meanings of many words and understand them in many combinations. Hans Moravec, a doctoral student of McCarthy at the time, stated that computers were still millions of times too weak to exhibit intelligence. As patience dwindled so did the funding, and research came to a slow roll for ten years.

In the 1980s, AI was reignited by two sources: an expansion of the algorithmic toolkit, and a boost of funds. John Hopfield and David Rumelhart popularized deep learning techniques which allowed computers to learn using experience. On the other hand Edward Feigenbaum introduced expert systems which mimicked the decision making process of a human expert. The program would ask an expert in a field how to respond in a given situation, and once this was learned for virtually every situation, non-experts could receive advice from that program. Expert systems were widely used in industries. The Japanese government heavily funded expert systems and other AI related endeavors as part of their Fifth Generation Computer Project (FGCP). From 1982-1990, they invested $400 million dollars with the goals of revolutionizing computer processing, implementing logic programming, and improving artificial intelligence. Unfortunately, most of the ambitious goals were not met. However, it could be argued that the indirect effects of the FGCP inspired a talented young generation of engineers and scientists. Regardless, funding of the FGCP ceased, and AI fell out of the limelight.

Ironically, in the absence of government funding and public hype, AI thrived. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBMs Deep Blue, a chess playing computer program. This highly publicized match was the first time a reigning world chess champion loss to a computer and served as a huge step towards an artificially intelligent decision making program. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows. This was another great step forward but in the direction of the spoken language interpretation endeavor. It seemed that there wasnt a problem machines couldnt handle. Even human emotion was fair game as evidenced by Kismet, a robot developed by Cynthia Breazeal that could recognize and display emotions.

We havent gotten any smarter about how we are coding artificial intelligence, so what changed? It turns out, the fundamental limit of computer storage that was holding us back 30 years ago was no longer a problem. , which estimates that the memory and speed of computers doubles every year, had finally caught up and in many cases, surpassed our needs. This is precisely how Deep Blue was able to defeat Gary Kasparov in 1997, and how Googles Alpha Go was able to defeat Chinese Go champion, Ke Jie, only a few months ago. It offers a bit of an explanation to the roller coaster of AI research; we saturate the capabilities of AI to the level of our current computational power (computer storage and processing speed), and then wait for Moores Law to catch up again.

We now live in the age of big data, an age in which we have the capacity to collect huge sums of information too cumbersome for a person to process. The application of artificial intelligence in this regard has already been quite fruitful in several industries such as technology, banking, marketing, and entertainment. Weve seen that even if algorithms dont improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. There may be evidence that Moores law is slowing down a tad, but the increase in data certainly hasnt lost any momentum. Breakthroughs in computer science, mathematics, or neuroscience all serve as potential outs through the ceiling of Moores Law.

So what is in store for the future? In the immediate future, AI language is looking like the next big thing. In fact, its already underway. I cant remember the last time I called a company and directly spoke with a human. These days, machines are even calling me! One could imagine interacting with an expert system in a fluid conversation, or having a conversation in two different languages being translated in real time. We can also expect to see driverless cars on the road in the next twenty years (and that is conservative). In the long term, the goal is general intelligence, that is a machine that surpasses human cognitive abilities in all tasks. This is along the lines of the sentient robot we are used to seeing in movies. To me, it seems inconceivable that this would be accomplished in the next 50 years. Even if the capability is there, the ethical questions would serve as a strong barrier against fruition. When that time comes (but better even before the time comes), we will need to have a serious conversation about machine policy and ethics (ironically both fundamentally human subjects), but for now, well allow AI to steadily improve and run amok in society.

Rockwell Anyoha is a graduate student in the department of molecular biology with a background in physics and genetics. His current project employs the use of machine learning to model animal behavior. In his free time, Rockwell enjoys playing soccer and debating mundane topics.

This article is part of a Special Edition on Artificial Intelligence.

Brief Timeline of AI

https://www.livescience.com/47544-history-of-a-i-artificial-intelligence-infographic.html

Complete Historical Overview

http://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf

Dartmouth Summer Research Project on Artificial Intelligence

https://www.aaai.org/ojs/index.php/aimagazine/article/view/1904/1802

Future of AI

https://www.technologyreview.com/s/602830/the-future-of-artificial-intelligence-and-cybernetics/

Discussion on Future Ethical Challenges Facing AI

http://www.bbc.com/future/story/20170307-the-ethical-challenge-facing-artificial-intelligence

Detailed Review of Ethics of AI

https://intelligence.org/files/EthicsofAI.pdf

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The History of Artificial Intelligence - Science in the News

1. AI Trends - The Best Source for AI News and Events

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Top 75 Artificial Intelligence Websites & Blogs For AI ...

Automation, robotics and the use of sophisticated computer software and programs characterize a career in artificial intelligence (AI). Candidates interested in pursuing jobs in this field require specific education based on foundations of math, technology, logic, and engineering perspectives. Written and verbal communication skills are also important to convey how AI tools and services are effectively employed within industry settings. To acquire these skills, those with an interest in an AI career should investigate the various career choices available within the field.

The most successful AI professionals often share common characteristics that enable them to succeed and advance in their careers. Working with artificial intelligence requires an analytical thought process and the ability to solve problems with cost-effective, efficient solutions. It also requires foresight about technological innovations that translate to state-of-the-art programs that allow businesses to remain competitive. Additionally, AI specialists need technical skills to design, maintain and repair technology and software programs. Finally, AI professionals must learn how to translate highly technical information in ways that others can understand in order to carry out their jobs. This requires good communication and the ability to work with colleagues on a team.

Basic computer technology and math backgrounds form the backbone of most artificial intelligence programs. Entry level positions require at least a bachelors degree while positions entailing supervision, leadership or administrative roles frequently require masters or doctoral degrees. Typical coursework involves study of:

Candidates can find degree programs that offer specific majors in AI or pursue an AI specialization from within majors such as computer science, health informatics, graphic design, information technology or engineering.

A career in artificial intelligence can be realized within a variety of settings including private companies, public organizations, education, the arts, healthcare facilities, government agencies and the military. Some positions may require security clearance prior to hiring depending on the sensitivity of information employees may be expected to handle. Examples of specific jobs held by AI professionals include:

From its inception in the 1950s through the present day, artificial intelligence continues to advance and improve the quality of life across multiple industry settings. As a result, those with the skills to translate digital bits of information into meaningful human experiences will find a career in artificial intelligence to be sustaining and rewarding.

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What Skills Do I Need to Get a Job in Artificial Intelligence?

Artificial Intelligence Computers are everywhere today. It would be impossible to go your entire life without using a computer. Cars, ATMs, and TVs we use everyday, and all contain computers. It is for this reason that computers and their software have to become more intelligent to make our lives easier and computers more accessible. Intelligent computer systems can and do benefit us all; however people have constantly warned that making computers too intelligent can be to our disadvantage. Artificial intelligence, or AI, is a field of computer science that attempts to simulate characteristics of human intelligence or senses. These include learning, reasoning, and adapting. This field studies the designs of intelligentshow more content

Expert systems are also known as knowledge based systems. These systems rely on a basic set of rules for solving specific problems and are capable of learning. The laws are defined for the system by experts and then implemented using if-then rules. These systems basically imitate the experts thoughts in solving the problem. An example of this is a system that diagnosis medical conditions. The doctor would input the symptoms to the computer system and it would then ask more questions if need or give diagnoses. Other examples include banking systems for acceptance of loans, advanced calculators, and weather predictions. Natural language systems interact allow computers to interact with the user in their usual language. They accept, interpret, and execute the commands in this language. The attempt is to allow a more natural interaction between the computer and user. Language is sometimes thought to be the foundation of intelligence in humans. Therefore, it is reasonable for intelligent systems to be able to understand language. Some of these systems are advanced enough to hold conversations. A system that emulates human senses uses human sensory simulation. These can include methods of sight, sound, and touch. A very common implementation of this intelligence is in voice recognition software. It listens to what the user says, interprets the sounds, and displays the information on the screen. These are

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Artificial Intelligence Essay - 966 Words | Bartleby

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.

Got a question for us? Please mention it in the comments section of Artificial Intelligence Tools & Frameworks and we will get back to you.

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Top 12 Artificial Intelligence Tools & Frameworks | Edureka

NIST has releaseda plan for prioritizing federal agency engagement in the development of standards for artificial intelligence (AI)per the February 2019 Executive Order on Maintaining American Leadership on Artificial Intelligence (EO 13859). The plan recommends the federal government commit to deeper, consistent, long-term engagement in AI standards development activities to help the United States to speed the pace of reliable, robust, and trustworthy AI technology development.

It calls for federal agencies to bolster AI standards-related knowledge, leadership, and coordination among agencies that develop or use AI; promote focused research on the trustworthiness of AI systems; support and expand public-private partnerships; and engage with international parties.

NIST will participate in developing AI standards, along with the private sector and academia, that address societal and ethical issues, governance, and privacy policies and principles. These AI standards-related efforts include:

While the AI community has agreed that these issues must factor into AI standards, many decisions still need to be made about whether there is yet enough scientific and technical basis to develop those standards provisions.

For news about this plan, seehttps://www.nist.gov/news-events/news/2019/08/plan-outlines-priorities-federal-agency-engagement-ai-standards-development

To provide the technical expertise and help develop and administer many of the future AI standards activities and development, NISTs Information Technology Laboratory recently established an Associate Director for IT Standardization position.

The U.S. Leadership in AI: A Plan for Federal Engagement in Developing Technical Standards and Related Tools report released on August 9, 2019, was prepared with broad public and private sector input. The plan identifies nine areas of focus for AI standards:

Through these focus areas, the Federal government will commit to deeper, consistent, long-term engagement in AI standards development activities to help the United States to speed the pace of reliable, robust, and trustworthy AI technology development. Specifically, the government will:

NIST will play an active role in advancing the AI standards strategies. NISTs Information Technology Laboratory has recently established an Associate Director for IT Standardization position, which will help administer many of NISTs future AI standards activities and development.

See the rest here:

AI Standards | NIST

The Artificial Intelligence tutorial provides an introduction to AI which will help you to understand the concepts behind Artificial Intelligence. In this tutorial, we have also discussed various popular topics such as History of AI, applications of AI, deep learning, machine learning, natural language processing, Reinforcement learning, Q-learning, Intelligent agents, Various search algorithms, etc.

Our AI tutorial is prepared from an elementary level so you can easily understand the complete tutorial from basic concepts to the high-level concepts.

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.

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:

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.

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:

Following are the main goals of 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:

Following are some main advantages 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:

Before learning about Artificial Intelligence, you must have the fundamental knowledge of following so that you can understand the concepts easily:

Our AI tutorial is designed specifically for beginners and also included some high-level concepts for professionals.

We assure you that you will not find any difficulty while learning our AI tutorial. But if there any mistake, kindly post the problem in the contact form.

Read more from the original source:

AI Tutorial | Artificial Intelligence Tutorial - Javatpoint