Archil Cheishvili is CEO and co-founder at GenesisAI, a global network of artificial intelligence products and services.

With the latest advancements in artificial intelligence (AI), achieving human-like intelligence is gradually transitioning to the realm of possibility. And with disruptors like the Covid-19 pandemic ravaging the global economy, the race to achieve artificial general intelligence may have sped up significantly.

I would like to share some insights on the challenges and opportunities intertwined with the path to achieving AGI and the future ahead.

What is artificial general intelligence?

Artificial intelligence can be broadly categorized into three main types: artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial superintelligence (ASI). Amongst these, AGI positions artificial intelligence at par with human capabilities. As a result, AGI systems can think, comprehend, learn and apply their intelligence to solve problems much like humans would for a given situation.

The anthropomorphic capabilities that convert artificial intelligence into artificial general intelligence include:

• Sensory perception.

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• Fine motor skills.

• Natural language processing and understanding.

• Navigation.

• Problem-solving.

• Social and emotional engagement.

• Creativity.

In simpler words, if AGI is achieved, machines would be capable of understanding the world at the same capacity as any human being. And based on these external inputs, they can discover solutions to an ongoing problem.

Challenges In The Way Of Artificial General Intelligence

While AGI may not have been realized so far, it promises a world of fruitful possibilities. However, it is currently plagued with serious roadblocks, which are present in the form of the following:

• The lack of a working protocol to help with artificial intelligence or machine learning networking is problematic. This deficiency coerces systems to work as standalone models in a closed environment. And such a mode of operation is a stark contrast from the convoluted and highly social “human experience.”

• Communication gaps come in the way of seamless data sharing and the inter-learning of machine learning models, which reduces universality. 

• The absence of an artificial intelligence network also hinders the overall development of a common goal.

• Organizational executives are in the dark on how to integrate AI with their business operations to drive specific results.

• The lack of direction, complemented by the fact that companies cannot afford to hire a dedicated team of AI experts, makes the implementation of AI platforms costly.

• AI developers and companies often experience issues while selling their code and services.

How Can AGI Be Created?

There are three important goals that should be achieved in order to potentially create AGI.

1. We must connect companies in need of AI technologies with developers looking for monetization opportunities, which is made possible through an AI marketplace.

2. We should start interconnecting AI services and networks to create data lakes that can power AGI. The interactions between various AI platforms will help develop universal machine learning solutions.

3. We can begin democratizing access to AI technologies and challenging oligopolies to offer technologically advanced solutions for all.

These three goals can be achieved by setting up communication protocols for data and service exchanges, while also making AI more accessible through an end-to-end AI marketplace. The former helps to potentially lay the groundwork for AGI, while the latter connects companies and developers to reduce time to market.

Final Thoughts

The next decade will play a crucial role in accelerating the development of AGI. In fact, experts believe that there is a 25% chance of achieving human-like AI by 2030. Furthermore, advancements in robotic approaches and machine algorithms, paired with the recent data explosion and computing advancements, will serve as a fertile basis for human-level AI platforms. 

Now, it is only a matter of time until AGI becomes a part of the new normal.

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Business intelligence techniques help understand trends and identify patterns from big data

In the digital world, modern businesses generate big data on daily basis. The recent advancement in technology has opened the door for companies to effectively store and process big data to unleash data-driven decisions and insights. Unfortunately, there is a void between data storage and usage. Many companies, starting from small to big, collect huge data but only use very little of it to make business decisions. In order to mitigate this data gap, business intelligence is being deployed. With the rise in the need for real-time data processing, business intelligence techniques have exploded, making data and analytics accessible for more than just analysts. While business intelligence technology helps decision-makers to analyze data and make informed decisions, top business intelligence techniques drive the initiatives. They help analysts understand trends and aid them to identify patterns in the mountains of big data that businesses build up. The need for more disruption in decision-making and the growing demand for business intelligence has opened the door for a surplus amount of business intelligence techniques. In this Article, Analytics Insight has listed top business intelligence techniques that help companies to get the maximum out of big data.

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Top Business Intelligence Techniques

OLAP

Online Analytical Processing (OLAP) is an important business intelligence technique, that is used to solve analytical problems with different dimensions. A major benefit of using OLAP is that its multi-dimensional nature provides leniency for users to look at data issues from different views. By doing so, they can even identify hidden problems in the process. OLAP is mainly used to complete tasks like budgeting, CRM data analysis, and financial forecasting.

Data Visualization

Data is often stored in form of numbers that are put together as a matrix. But interpreting the matrix to make business decisions is a critical task. A commoner, or even an analyst, can find the progress of data when it is stored as a set. To untangle the knot, data visualization is used. Data visualizations help professionals look at data from more than one dimension and help them make informed decisions. Therefore, visualization of data in charts is an easy and convenient way to understand the stance.

Data Mining

Data mining is the process of analyzing large quantities of data to discover meaningful patterns and rules by automatic or semi-automatic means. In a corporate data warehouse, the amount of data stored is very huge. Finding the actual data that could drive business decisions is quite critical. Therefore, analysts use data mining techniques to unravel the hidden patterns and relationships in data. Knowledge discovery in databases is the whole process of using the database along with any required selection, processing, sub-sampling, choosing the proper way for data transformation.

Reporting

Reporting in business intelligence represents the whole process of designing, scheduling, generating the performance, sales, reconciliation, and saving the content. It helps companies to effectively gather and present information to stand by the management, planning, and decision-making process. Business leaders get to view the reports at daily, weekly, or monthly intervals as per their needs.

Analytics

Analytics in Business Intelligence defines the study of data to extract effective decisions and figure out the trends. Analytics is famous among business companies as it lets analysts and business leaders deeply understand the data they have and drive value from it. Many business perspectives, from marketing to call centers to use analytics in different forms. For example, call centers leverage speech analytics to monitor customer sentiments and improve the way answers are presented.

Multi-Cloud

Following the outbreak of the pandemic and the lockdown that came to effect, companies across the globe started moving their routine working into cloud modes. The rise of cloud technology has greatly impacted many businesses. However, even after the restrictions are lifted, companies still prefer to work over the cloud because of its lenient accessibility and easy-to-use attributes. Moving a step forward, even Research & Development initiatives are being moved to the cloud, thanks to its cost-saving and easy-to-use nature.

ETL

Extraction-Transaction-Loading (ETL) is a unique business intelligence technique that takes care of the overall data processing routine. It extracts data from storage, transforms it into the processor, and loads it into the business intelligence system. They are mainly used as a transaction tool that transforms data from various sources to data warehouses. ETL also moderates the data to address the need of the company. It improves the quality level by loading it into the end targets such as databases or data warehouses.

Statistical Analysis

Statistical analysis uses mathematical techniques to create the significance and reliability of observed relations. It also grasps the change of behavior in people that are visible in data with its distribution analysis and confidence intervals. Post data mining, analysts carry out statistical analysis to devise and get effective answers.

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The Big Data Technology & Services Market report gives an outline of the current market Trend, gradual income, and future viewpoint of the Big Data Technology & Services Market.

According to the report, the Global Big Data Technology & Services Market is foreseen to observe huge development during the gauge time frame from 2020 to 2027.

The report gives a brief outline and definite bits of knowledge into the market by gathering information from the business specialists and a few common on the lookout. Other than this, the report offers a point-by-point investigation of geological territories and depicts the serious situation to help financial specialists, conspicuous players, and new participants to get a significant portion of the worldwide Big Data Technology & Services market.

Our investigation includes the investigation of the market thinking about the effect of the COVID-19 pandemic. If it’s not too much trouble connect with us to get your hands on comprehensive inclusion of the effect of the current circumstance available.

The report presents an outline of each market section, for example, type, end-client, applications, and district. With the assistance of pie diagrams, diagrams, correlation tables, and progress graphs a total review of the piece of the overall industry, size, and income, and development designs are open in the report.

Also, a diagram of each market section, for example, end-client, item type, application, and the district is offered in the report. The market across different locales is investigated in the report which incorporates North America, Europe, Asia-Pacific, and LAMEA. The report clarifies future patterns and development openings in each district. These bits of knowledge help in understanding the worldwide patterns on the lookout and structure procedures to be actualized later on. Also, the exploration report profiles a portion of the main organizations in the worldwide Big Data Technology & Services industry. It makes reference to their essential activities and offers a brief about their business. A portion of the players profiled in the worldwide Big Data Technology & Services market include:

Vital participants in the Big Data Technology & Services covers: Accenture PLC, Course5 Intelligence Pvt. Ltd., Fractal Analytics Inc., LatentView Analytics Pvt. Ltd., Mu Sigma Business Solutions Pvt. Ltd., Absolutdata Research & Analytics Solutions Pvt. Ltd., Cisco Systems Inc., Microsoft Corporation, Manthan Software Services Private Limited, International Business Machines Corporation, Oracle Corporation

Examiners have additionally expressed the innovative work exercises of these organizations and gave total data about their current items and administrations. Furthermore, the report offers a better view over various variables driving or compelling the improvement of the market.

The Big Data Technology & Services can be partly founded on item types, significant applications, and significant nations as follows:

The premise of utilizations, the Big Data Technology & Services from 2015 to 2027 covers: BFSI, Retail, Aerospace and Defense, Media and Entertainment, Healthcare and Pharmaceuticals, Others

The premise of types, the Big Data Technology & Services from 2015 to 2027 is fundamentally part of On-Premises, Cloud/On-Demand

The report obviously shows that the Big Data Technology & Services business has accomplished exceptional advancement since 2027 with various huge improvements boosting the development of the market. This report is readied dependent on a definite appraisal of the business by specialists. To finish up, partners, financial specialists, item chiefs, advertising heads, and different specialists looking for real information on inventory, requests, and future forecasts would discover the report important.

The report establishes: 

Section 1 gives an outline of the Big Data Technology & Services market, containing worldwide income, worldwide creation, deals, and CAGR. The estimate and examination of Big Data Technology & Services market by type, application, and district are likewise introduced in this part.

Part 2 is about the market scene and significant players. It gives serious circumstance and market fixation status alongside the fundamental data of these players.

Part 3 gives a full-scale investigation of significant parts in the Big Data Technology & Services industry. The fundamental data, just as the profiles, applications, and details of items market execution alongside Business Overview are advertised.

Section 4 gives an overall perspective on the Big Data Technology & Services market. It incorporates creation, piece of the overall industry income, cost, and the development rate by type.

Part 5 spotlights on the utilization of Big Data Technology & Services, by breaking down the utilization and its development pace of every application.

Part 6 is about the creation, utilization, fare, and import of Big Data Technology & Services in every district.

Section 7 focuses on the creation, income, cost, and gross edge of Big Data Technology & Services in business sectors of various districts. The examination on creation, income, cost, and the gross edge of the worldwide market is canvassed in this part.

Section 8 focuses on assembling examination, including key crude material investigation, cost structure examination, and cycle investigation, making up a thorough examination of assembling cost.

Section 9 presents the modern chain of Big Data Technology & Services. Modern chain examination, crude material sources, and downstream purchasers are investigated in this section.

Section 10 gives clear experiences into market elements.

Section 11 possibilities the entire Big Data Technology & Services market, including the worldwide creation and income gauge, territorial figure. It likewise predicts the Big Data Technology & Services market by type and application.

Section 12 closes the examination discoveries and refines all the features of the investigation.

Section 13 presents the examination philosophy and wellsprings of exploration information for your arrangement.

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Network interconnect supports command and control, Internet of Things, cloud, storage replication, artificial intelligence (AI) and machine learning.

ASHBURN, Va. – Curtiss-Wright Corp. Defense Solutions division in Ashburn, Va., is introducing the PacStar 448 10-Gigabit Ethernet switch module for military, intelligence, and commercial applications.

The Ethernet switch delivers a 10x increase in networking speed to its PacStar Modular Data Center (MDC) 2.0. The PacStar 448 module, based on Cisco ESS 9300 technology, supports high-speed switching for the MDC’s servers and storage devices.

It enables PacStar MDC to perform compute and network tasks in tactical and expeditionary settings. The module features ten 10 Gigabit Ethernet SFP+ enhanced small form-factor pluggable transceiver ports that deliver speed and density.

The enhanced network interconnect performance provided by PacStar 448 supports command and control, Internet of Things, cloud, storage replication, artificial intelligence (AI) and machine learning. The module also is a drop-in replacement for previous Ethernet switch modules. Curtiss-Wright acquired PacStar last fall.

PacStar MDC is based on the PacStar 400-series commercial off-the-shelf (COTS) small-form-factor modules. It is a tactical and expeditionary rugged data center capable of hosting mission command, cloud storage, sensor fusion, AI, and analytics applications.

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Artificial intelligence (AI) has become a buzzword in technology in both civilian and military contexts. With interest comes a radical increase in extravagant promises, wild speculation, and over-the-top fantasies, coupled with funding to attempt to make them all possible. In spite of this fervor, AI technology must overcome several hurdles: it is costly, susceptible to data poisoning and bad design, difficult for humans to understand, and tailored for specific problems. No amount of money has eradicated these challenges, yet companies and governments have plunged headlong into developing and adopting AI wherever possible. This has bred a desire to determine who is “ahead” in the AI “race,” often by examining who is deploying or planning to deploy an AI system. But given the many problems AI faces as a technology its deployment is less of a clue about its quality and more of a snapshot of the culture and worldview of the deployer. Instead, measuring the AI race is best done by not looking at AI deployment but by taking a broader view of the underlying scientific capacity to produce it in the future.

AI Basics: The Minds We Create

AI is both a futuristic fantasy as well as an omnipresent aspect of modern life. Artificial intelligence is a wide term that broadly encompasses anything that simulates human intelligence. It ranges from the narrow AI already present in our day-to-day lives that focuses on one specific problem (chess playing programs, email spam filters, and Roombas) to the general artificial intelligence that is the subject of science fiction (Rachel from Blade Runner, R2-D2 in Star Wars, and HAL 9000 in 2001: A Space Odyssey). Even the narrow form that we currently have and continually improve, can have significant consequences for the world by compressing time scales for decisions, automating repetitive menial tasks, sorting through large masses of data, and optimizing human behavior. The dream of general artificial intelligence has been long deferred and is likely to remain elusive if not impossible, and most progress remains with narrow AI. As early as the 1950’s researchers were conceptualizing thinking machines and developed rudimentary versions of them that evolved into “simple” everyday programs, like computer opponents in video games.

Machine learning followed quickly, but underwent a renaissance in the early 21st century when it became the most common method of developing AI programs, to the extent that it has now become nearly synonymous with AI. Machine learning creates algorithms that allow computers to improve by consuming large amounts of data and using past “experience” to guide current and future actions. This can be done through supervised learning, where humans provide correct answers to teach the computer; unsupervised learning, where the machine is given unlabeled data to find its own patterns; and reinforced learning, where the program uses trial and error to solve problems and is rewarded or penalized based on its decision. Machine learning has produced many of the startling advances in AI over the last decade such as drastic improvements to facial recognition and self-driving cars, and has given birth to a method that seeks to use the lessons of biology to create systems that process data similar to brains: deep learning. This is characterized by artificial neural networks where data is broken down to be examined by “neurons” that individually handle a specific question (e.g. whether an object in a picture is red) and describes how confident it is in its assessment, and the network compiles these answers for a final assessment.

But despite the advances that AI has undergone since the machine learning renaissance and its nearly limitless theoretical applications, it remains opaque, fragile, and difficult to develop.

Challenges: The Human Element

The way that AI systems are developed naturally creates doubts about their ability to function in untested environments, namely the requirement of large amounts of data inputs, the necessity that they be nearly perfect, and the effects of the preconceived notions of its creators. First, lack of, or erroneous, data is one of the largest challenges, especially when relying on machine learning techniques. To teach a computer to recognize a bird, it must be fed thousands of pictures to “learn” a bird’s distinguishing features, which naturally limits use in fields with few examples. Additionally, if even a tiny portion of the data is incorrect (as little as 3%), the system may develop incorrect assumptions or suffer drastic decreases in performance. Finally, the system may also recreate assumptions and prejudices—racist, sexist, elitist, or otherwise—from extant data that already contains inherent biases, such as resume archives or police records. These could also be coded in as programmers inadvertently impart their own cognitive biases into the machine learning algorithms they design.

This propensity for deep-seated decision-making problems, which may only become evident well after development, will prove problematic to those that want to rely heavily on AI, especially concerning issues of national security. Because of the inherent danger of ceding critical functions to untested machines, plans to deploy AI programs should not be seen primarily as a reflection of their own quality, but of an organization’s culture, risk tolerance, and goals.

The acceptability of some degree of uncertainty also exacerbates the difficulties in integrating AI with human overseers. One option is a human-in-the-loop system where human overseers are integrated throughout the decision process. Another is human-on-the-loop system where the AI remains nearly autonomous with only minor human oversight. In other words, organizations must decide whether to give humans the ability to override a machine’s possibly better decision that they cannot understand. The alternative is to cede human oversight that may prevent disasters that might be obvious to organic minds. Naturally, the choice will depend on the stakes: militaries may be much more likely to allow a machine to control leave schedules without human guidance rather than anti-missile defenses.

Again, as with doubt about decision integrity, the manner in which an organization integrates AI into the decision-making process can tell us a great deal. Having a human-in-the-loop system signals that an organization would like to improve the efficiency of a system considered mostly acceptable as is. A human-on-the-loop system signals greater risk tolerance, but also betrays a desire to exert more effort to catch up to, or surpass, the state of the art in the field.

The Global AI Race: Measuring the Unmeasurable

Research and development funding is a key component of scientific advances in the modern world, and is often relied on as a metric to chart progress in AI. The connection is often specious, however; the scientific process is often filled with dead ends, ruined hypotheses, and specific research questions with no broader significance. This last point is particularly salient to artificial intelligence because of the tailored nature of specific AI applications, which requires a different design for each problem it tackles. AI that directs traffic, for example, is completely worthless at driving cars. For especially challenging questions (e.g. planning nuclear strategy), development is an open-ended financial commitment with no promise of results.

It becomes difficult, therefore, to accurately assess achievement by simply using the amount spent on a project as a proxy for progress. Perhaps money is being spent on dead ends, an incorrect hypothesis, or even to fool others into thinking that progress is being made. Instead, we should see money as a reflection of what the spender values. Project spending then is not an effective metric of the progress of AI development, but of how important a research question is to the one asking it.

But that importance provides a value for analysis, regardless of its inapplicability to measuring the AI race: the decision-making process can speak volumes about the deployer’s priorities, culture, risk tolerance, and vision. Ironically, the manner in which AI is deployed says far more about the political, economic, and social nature of the group deploying it than it does about technological capability or maturity. In that way, deployment plans offer useful information for others. This is particularly valid in examinations of government plans. Examination of plans have produced insight such as using Chinese AI documents to deduce where they see weakness in their own IT economy, finding that banks overstate the use of chatbots to appear convenient for their customers, or noting that European documents attempt to create a distinctive European approach to the development of AI in both style and substance. It is here that examinations of AI deployment plans offer their real value.

There are instead much better ways to measure progress in AI. While technology rapidly changes, traditional metrics of scientific capacity provide a more nuanced base to measure AI from and are harder to manipulate, which makes them more effective than measuring the outputs of AI projects. The most relevant include: scientists as a proportion of population, papers produced and number of citations, research and development spending generally (as opposed to the focus on specific projects), and number of universities and STEM students. Measuring any scientific process is naturally fraught with peril due to the potential for dead-end research, but taken broadly these metrics give a far better picture of the ability of a state or organization to innovate in AI technology. Multiple metrics should always be used however; any focus on a specific metric (e.g. research spending) will make it just as easy to game the system as relying on AI deployment does. Such a narrow focus also distorts the view of the AI landscape. Consider, for example, the intense insecurity over the position of the United States despite its continuing leadership in terms of talent, number of papers cited, and quality of universities.

Recharging the Scientific Base

The U.S. National Security Commission on AI draft report notes, “The nation with the most resilient and productive economic base will be best positioned to seize the mantle of world leadership.” This statement encapsulates the nature of the AI race, and naturally, measuring it. If a government or a company wishes to take a leadership position in the race, the goal should be to stimulate the base that will produce it, not actively promote a specific project, division, or objective. This involves tried and true (but oft neglected) policies like promoting STEM education, training new researchers internally, attracting foreign talent with incentives, providing funding for research and development (especially if it forms a baseline for future work such as computer security or resilience), and ensuring that researchers have access to the IT hardware that they need through adequate manufacturing and procurement processes.

These suggestions are often neglected in the United States in particular because of intense politicization of domestic priorities such as education policy (affecting universities), immigration policy (affecting the attraction of foreign talent), and economic policy (affecting manufacturing and procurement). At the same time, it is not only about providing more funding but streamlining processes that enable scientific capacity. For example, the system for receiving scientific research grants is byzantine, time-consuming, and stifling with different government agencies having overlapping funding responsibilities. Efforts should be made to ensure that applying for grants is not only easier, but that it promotes broader scientific inquiries. By solving problems like these, leaders invest in the components that will create the winning position in the AI race, and observers can determine who is making the strides to lead now, as well as in the future.

In the information age, the deployment of new technologies and their level of advancement have become key metrics in measuring power and effectiveness, but these are often flawed. Particularly for AI projects, research budgets, task assignments, and roles relative to humans demonstrate little about the state of the technology itself. Given the many fundamental problems with deploying AI, risk tolerance and strategic culture play much more of a role in determining how it is carried out: the more risk tolerant an organization is and the more it feels challenged by competitors, the more likely it will adopt AI for critical functions. Rather than examining AI deployment plans to see which country or organization is “ahead,” we should use them to study their worldview and strategic outlook. Instead, we should rely on overall scientific capacity to determine pole positions in the AI race.

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Thanks to the technology that we have at our hands these days, our everyday lives are much easier. We are able to complete multiple tasks without even leaving the comfort of our homes, stay updated on the latest news, and much more. Medicine was one of the areas that progressed immensely due to technological advancements, and we couldn’t be happier about it.

People receive the proper care, get accurate diagnostics, and the devices used to make every service both effective and efficient. In the past couple of years, there have been talks of implementing artificial intelligence in the medicinal sector as a way to improve this industry and make it near-perfect. We wanted to discuss the question of how can AI help this sector, but we are also going to take a look at one industry where AI is used to the fullest potential.

Where is AI Used The Best?

One of the industries that have managed to incorporate this technology and use its full potential is the online casino industry. Casino sites use artificial intelligence to protect their players, but also to enforce fair-play. Let us explain how.

In order for every player to have equal chances of winning, online casinos use Random Number Generators. This AI system creates random outcomes of each game and gives equal chances of winning to all players. Casimba Casino is a good example of an online casino that features this and the security AI, which we are about to explain.

The AI-powered security system at the aforementioned casino and many other casino sites goes by the name SSL-encryption software. This software takes all the data from the players and turns it into an unbreakable code, thus making it impossible for unwanted third parties to gain access. Both of these AI systems utilize algorithms to ensure safety and fair-play.

How Wil AI Help Medicine?

Through the use of algorithms, medicine can reap great benefits. Medicine sites can use SSL certificates to keep their patients’ data safe and out of harm’s way. Not only that, but this type of AI can also impact other areas such as radiology, pathology, cardiology, and ophthalmology. How? The algorithms are ever-learning and can analyze the data from various patients much faster than a doctor can. Then, by comparing it with other diagnoses, it can aid the doctor and pinpoint the exact treatment needed for that particular case.

These AI systems would aid in practices like diagnosis, treatment protocol development, patient monitoring, drug development, personalized medicine, and care.

Being efficient in this line of work is very important. Additionally, AI has the potential to be less prone to errors, which is a big advantage, especially when it comes to determining the diagnosis and the right treatment.

The only problem here is; AI is still in its development stages and authorities do not trust it that much to give it such an important role. While basic artificial intelligence is used in some sectors, many believe that it is still early to fully incorporate it. But, as technology keeps evolving, we do not doubt that AI will help medicine become much more effective.

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“Depending on who you ask, AI is either man’s greatest invention since the discovery of fire”, as Google’s CEO said at Google’s I/O 2017 keynote, or it is a technology that might one day make man superfluous. What’s inarguable is major companies have embraced AI as if it was one of the most important discoveries ever invented. In the US, Amazon, Apple, Microsoft, Facebook, IBM, SAS, and Adobe have all infused AI and machine learning throughout their operations, while in China the big four – Baidu, Alibaba, Tencent, Xiaomi – are coordinating with the government and all working on unique and almost siloed AI initiatives.

In her article Understanding Three Types of Artificial Intelligence, Anjali UJ explains “The term AI was coined by John McCarthy, an American computer scientist in 1956.” Anjali speaks of the following three types of AI, including:

1. Narrow Artificial Intelligence: AI that has been trained for a narrow task.
2. Artificial General Intelligence: AI containing generalized cognitive abilities, which understand and reason the environment the way humans do.
3. Artificial Super Intelligence: AI that surpasses human intelligence and allows machines to mimic human thought.

AI is not a new technology, in reality, it’s decades old. In his MIT Technology Review article Is AI Riding a One-Trick Pony?, James Somers states “Just about every AI advance you’ve heard of depends on a breakthrough that’s three decades old.” Recent advances in chip technology, as well as improvements in hardware, software, and electronics have turned AI’s enormous potential into reality.

Neural Nets

AI is founded on Artificial Neural Networks (ANN) or just “Neural Nets”, which are non-linear statistical data modelling tools used when the true nature of a relationship between input and output is unknown. In his article Machine Learning Applications for Data Center Optimization, Jim Gao describes neural nets as “a class of machine learning algorithms that mimic cognitive behavior via interactions between artificial neurons.” Neural nets search for patterns and interactions between features to automatically generate a best­ fit model.

They do not require the user to predefine a model’s feature interactions. Speech recognition, image processing, chatbots, recommendation systems, and autonomous software agents are common examples of machine learning. There are three types of training in neural networks; supervised, which is the most common, as well as unsupervised training and reinforcement learning. AI can be broken down into three areas:

Machine Learning

A branch of computer science, machine learning explores the composition and application of algorithms that learn from data. These algorithms build models based on inputs and use those results to predict or determine actions and results, rather than following strict instructions.

Supervised learning’s goal is to learn a general rule that maps inputs to outputs and the computer is provided with example inputs as well as the desired outputs. With unsupervised learning, however, labeled data isn’t provided to the learning algorithm and it must find the input’s structure on its own. In reinforcement learning, the computer utilizes trial and error to solve a problem. Like Pavlov’s dog, the computer is rewarded for good actions it performs and the goal of the program is to maximize reward.

Deep learning

A subset of machine learning, deep learning utilizes multi-layered neural nets to perform classification tasks directly from image, text, and/or sound data. In some cases, deep learning models are already exceeding human-level performance. Google Meet’s ability to transcribe a human voice during a live conference call is an example of deep learning’s impressive capabilities.

ML and deep learning are useful for personalization marketing, customer recommendation, spam filtering, fraud detection, network security, optical character recognition (OCR), computer vision, voice recognition, predictive asset maintenance, sentiments analysis, language translations, and online search, among others.

7 Patterns of AI

In her Forbes article The Seven Patterns of AI, Kathleen Walch lays out a theory that, regardless of the application of AI, there are seven commonalities to all AI applications. These are “hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems.” Walch adds that, while AI might require its own programming and pattern recognition, each type can be combined with others, but they all follow their own pretty standard set of rules.

The ‘Hyperpersonalization Pattern’ can be boiled down to the slogan, ‘Treat each customer as an individual’. ‘Autonomous systems’ will reduce the need for manual labor. Predictive analytics portends “some future value for data, predicting behavior, predicting failure, assisted problem resolution, identifying and selecting best fit, identifying matches in data, optimization activities, giving advice, and intelligent navigation,” says Walch. The ‘Conversational Pattern’ includes chatbots, which allow humans to communicate with machines via voice, text, or image.

The ‘Patterns and Anomalies’ type utilizes machine learning to discern patterns in data and it attempts to discover higher-order connections between data points, explains Walch. The recognition pattern helps identify and determine objects within image, video, audio, text, or other highly unstructured data notes Walch. The ‘Goal-Driven Systems Pattern’ utilizes the power of reinforcement learning to help computers beat humans on some of the most complex games imaginable, including Go and Dota 2, a complicated multiplayer online battle arena video game.

^Conclusion

A few years ago, the AI hype had reached such a fever pitch that companies just had to add ‘AI’, ‘ML’, or ‘Deep Learning’ to their pitch decks, and funding flooded through the door. However, businesses are investing in AI powered solutions like AIOps to reduce IT operations cost. Today, investors are a little wiser to the fact that not all that glitters is AI gold, and a lot of companies who pitched themselves as AI experts really didn’t know the difference between a neural net and a k-means algorithm.

Jumping head-first into AI is a recipe for disaster. Only “1 in 3 AI projects are successful and it takes more than 6 months to go from concept to production, with a significant portion of them never making it to production—creating an AI dilemma for organizations,” says Databricks. Not only is AI old, but it is also a difficult technology to implement. Anyone delving into AI needs to have a strong understanding of technology, what it is, where it came from, what limitations might hold it back, so although AI is exceptional technology, the waters are deep. It is far from the panacea that many software companies claim it is. AI has had not one but two AI winters. CEOs looking to make a substantial investment in AI should be well aware of the old saying that ‘a fool and his money are easily parted’, as that fool could be an AI fool, too.

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As data centers become enabler to a nation’s economy, employing artificial intelligence can yield higher benefits

Artificial Intelligence (AI) plays a pivotal role in capturing, processing, and analyzing data at much faster rate than ever, today! It is also becoming more efficient and useful to incorporate data elements and managing data centers.

With data becoming a pre-requisite to sustain almost every business operation for insight and business results, data centers are on the crux of this digital transformation. These physical facilities that house the computers and equipment power the information needs of the modern economy. Data centers provide seamless data backup and recovery facilities while supporting cloud storage applications and transactions. Apart from boosting economy, the data center ecosystem attracts many international tech companies for the nation. Moreover, the presence of data centers ensure an excellent investment climate and employment opportunities for the local community.

Despite their key role in bringing digital revolution, they are not without problems. According to Gartner analyst Dave Cappuccio, 80% of enterprises will shut down their traditional data centers by 2025. The figures are fitting considering the host of problems faced by traditional data centers like lack of readiness to upgrade, infrastructure challenges, environmental issues and more. And the remedy for this is leveraging artificial intelligence to enhance the data center functions and infrastructure.

As per a Forbes Insights report, in early 2020, artificial intelligence is poised to have a tremendous impact on data center management, productivity, and infrastructure. Meanwhile, its technologies continue to offer data centers’ potential solutions to improve operations over the long term. In return data centers enabled by accelerated computing capabilities of AI, would be able to process AI workloads more efficiently.

Data centers consume a lot of energy, so training an artificial intelligence network to improve power usage effectiveness (PUE) is a key goal. PUE is essential metric to measure data center efficiency. In 2014 by deploying Deepmind AI in one of its facilities, Google was able to consistently achieve a 40% reduction in the amount of energy used for cooling, which equated to a 15% reduction in overall PUE overhead after accounting for electrical losses and other non-cooling inefficiencies. It also produced the lowest PUE the site had ever seen. Deepmind analyzes over 100 different variables within the data center to improve efficiency and reduce power consumption.

Data centers are also susceptible to various cyber threats. Cybercriminals are always finding new ways to obtain data from data centers or launch their next data breach attack. By learning normal network behavior and detecting cyber threats based on deviation from that behavior, artificial intelligence proves to be resourceful again!  Artificial algorithms can complement current Security Incidents and Event Management (SIEM) systems, by analyzing incidents and inputs from multiple systems, and devising an appropriate incident response system.

In a data center, IT devices are often deployed or removed from shelves that brings a lot of fragmented resources, like U space, which cannot be monitored or managed, and are easy to get wasted. By using intelligent hardware and IoT sensors, artificial intelligence allows effective data center infrastructure management that keeps a close eye on the data center and reduces repetitive work through automation. Here, data center managers can automate activities like temperature management, equipment status monitoring, floor security, fire hazards mitigation, ventilation, and cooling systems management. Coupled with predictive analytics, automation also helps in predictive maintenance at data centers.

Further, this AI-based predictive analysis can help data centers distribute workloads across the many servers in the firm. As a result, it will be easy to predict and manage data center loads more efficiently. It will also help in optimizing server storage systems, finding possible fault points in the system, improve processing times and reducing risk factors much faster.

Recently, MIT researchers had developed an AI system that automatically learns how to schedule data-processing operations across thousands of servers. This system was observed to be about 20 to 30% faster, and twice as fast during high-traffic times in completing key data center tasks. The researchers asserts that this artificial intelligence system could enable data centers to handle the same workload at higher speeds, using fewer resources.

Additionally, through deep learning (DL) applications, AI can predict failures and outages ahead of time. E.g.  HPE artificial intelligence predictive engine helps in identifying and resolving bottlenecks in the data center.  A survey of 200 companies highlighted that downtime results in losses surpassing US$26.5 billion, with the cost per minute of a network outage reaching approximately US$7,900. By monitoring server performance, network congestions, and disk utilization, AI can detect and predict data outages. Besides, it can implement mitigation strategies to help the data center recover from the data outage – thus adding to customer satisfaction and minimal losses during such outages.

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New Treasury Department software points the way. But research suggests that it’s impossible to show that an artificial ‘superintelligence’ can be contained

If, like me, you’re worried about how members of Congress are supposed to vote on a stimulus bill so lengthy and complex that nobody can possibly know all the details, fear not — the Treasury Department will soon be riding to the rescue.

But that scares me a little too.

Let me explain. For the past few months, the department’s Bureau of the Fiscal Service has been testing software designed to scan legislation and correctly allocate funds to various agencies and programs in accordance with congressional intent — a process known as issuing Treasury warrants. Right now, human beings must read each bill line by line to work out where the money goes. If the program can be made to work, the savings will be significant.

Alas, there’s a big challenge. Plenty of tools exist for extracting data from HTML files (and, of course, XML files), but Congress initially publishes legislation only in PDF form; XML or HTML versions often arrive only weeks later. As many a business knows, scraping data from PDFs generally requires human intervention, leading to the possibility of copy errors. The trouble is that PDFs have no standard data format. Even “simple” methods for extraction generally are designed to work only if the data in question is already presented within the PDF in tabular form.

Treasury’s ambitious hope, however, is that its software, when fully operational, will be able to scan new legislation in its natural language form, figure out where the money is supposed to go and issue the appropriate warrants far more swiftly than humans could. The faster the warrants are issued, the sooner the agency that’s supposed to receive the money can start spending.

Pretty cool stuff.

Yet this snapshot of the future inspires a wicked train of thought. Suppose that the Treasury Department software — which you are free to describe as artificial intelligence or not, depending on your taste — is later replaced by a better program, then by a better one and finally by one that can mimic the working general intelligence of the human mind.

What’s to stop this future AI from deciding on its own that Congress was wrong to give another billion to Agency A when, in the judgment of the program, Agency B needs it more? The program makes a tiny adjustment in a gigantic spending bill, and given that nobody’s actually read it, nobody’s the wiser.

Sounds improbable, right? HAL 9000 meets “Person of Interest” meets Skynet?

Not so fast.

For technophiles like me, recent achievements in AI are exciting, even breathtaking. AI is credited with reorganizing supply chains to help overcome disruptions caused by the pandemic. Deep learning systems may be able to discover coronary plaques more accurately than clinicians.

So why worry? After all, most of those in the field, including my professors when I studied artificial intelligence as an undergraduate, are confident that tight programming will keep even the most advanced artificial intelligence from escaping the bounds set by its creators. (Think Isaac Asimov’s Laws of Robotics.)

But there have long been dissenters, even among the experts. The prospect of an out-of-control AI has haunted researchers in the field for almost as long as it’s haunted science fiction writers. One thinks of Joseph Weizenbaum’s “Computer Power and Human Reason,” published back in 1976, or even Norbert Wiener’s classic “God and Golem, Inc.,” based on lectures the author delivered in 1962.

All of which brings us to an unnerving paper published last month by six AI researchers who argue that it is impossible to show that an artificial “superintelligence” can be contained. The authors are an international group, representing universities in Germany, Spain, and Chile, as well as the U.S. According to their analysis, no matter how tightly an AI may be programmed, if it indeed possesses generalized reasoning skills “far surpassing” those of the most gifted humans, what they call “total containment” turns out to be incapable of formal proof.

Using what is known as computability theory, they hypothesize a superintelligent AI that incorporates a fundamental command never to harm humans. (Asimov again.) The programming will then require a function that decides whether a particular action will harm humans or not. They proceed to show that even if it’s possible “to articulate in a precise programming language” a perfect set of “control strategies” to implement this function, there’s no way to know for sure whether the strategies will in fact constrain the AI. (The proof, although technical, is rather elegant, and fun to read.)

Don’t get me wrong: I’m not arguing that the Treasury Department should abandon its quest for a system that extracts data from PDFs, any more than I’m suggesting that any of the countless researchers working on various aspects of AI should halt. I continue to find the prospect of true artificial intelligence as exciting as ever.

What concerns me, however, is the way that public critiques of AI tend to pick around the edges rather than go to the heart of the matter. We often charge nascent AI systems with enhancing bias — for example, by exacerbating rather than correcting disparities in the distribution of health care. Such issues are of undeniable public importance. But as the authors of the paper on computability remind us, you don’t have to be either a technophobe or a fan of apocalyptic steampunk sci-fi to see that the time for public conversation about the containability of AI is now, not later.

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AI and ML in intelligent analytics can drive the efficiency in business

Analytics has been changing the way organizations operate for a long while. Since more organizations are dominating their utilization of analytics, they are diving further into their data to build proficiency, acquire a more prominent upper hand, and lift their bottom lines significantly more.

Analytics powers your business, however, what amount of value would you say you are truly harnessing from your data?

Artificial intelligence and machine learning can help. Artificial intelligence is a collection of technologies that extract patterns and valuable insights from huge datasets, then making forecasts dependent on that data. Truth be told, AI exists today that can assist you with getting more value out of the data you as of now have, bind together that data, and make forecasts about customer behaviors based on it.

The adoption of AI has been driven not just by increased computational power and new algorithms yet additionally the growth of data now accessible. For intelligence analysts, that multiplication of data implies surefire data over-burden. Human analysts essentially can’t adapt to that much information. They need assistance.

Intelligence leaders realize that AI can assist to adapt to this data downpour yet they may likewise consider what sway AI will have on their work and staff. For example, Twitter utilizes machine learning and AI to assess tweets in real-time and score them utilizing different measurements to show tweets that can possibly drive the most engagement.

Google is researching virtually every part of machine learning and is making advancements in old-style algorithms and different applications like speech translation, prediction systems, natural language processing, and search ranking.

Artificial intelligence plays a significant part in assisting organizations with handling data without forfeiting accuracy or speed.

With digital transformation widely being embraced, the volume and size of data have expanded significantly. Also, dealing with such gigantic data isn’t simple. Artificial intelligence- fueled data-driven innovation can help organizations manage such data to guarantee importance, worth, security, and transparency. They can depend on AI data integration platforms to ingest, change, and use information easily and with accuracy. Such platforms give an end-to-end encrypted environment that protects information from undesirable infringing and breaches, and make them hard to work with.

Artificial intelligence and ML frameworks exist that utilize analytics data to assist you with foreseeing results and effective blueprints. Artificial intelligence- empowered frameworks can analyze information from many sources and deliver forecasts about what works and what doesn’t. It can likewise deeply jump into information about your customers and offer predictions about buyer inclinations, marketing and sales channels, and product development strategies.

Artificial intelligence/ML advances empower companies across various industries to harness value from customer information with no trouble. For instance, AI data integration solutions empower all business users to map information between various fields to make it simpler to incorporate the data into a unified database. Since these arrangements can be effortlessly utilized by non-technical users, IT people need not assume full responsibility. This leaves IT to zero in on other vital tasks.

These solutions use ML algorithms to provide predictions of data, which can additionally quicken the data transformation process. Since the decisions are taken utilizing algorithms, the chance of mistakes like missing qualities, deceptions, errors, and so on, reduce. Hence, companies can use AI/ML tools to change the manner in which they deliver customer value. They can plan and integrate data and keep up data integrity, improving decision-making and boosting growth.

The advantages of AI and ML, notwithstanding, can go a long way beyond time savings. All things considered, intelligence work is a never-ending process; there is consistently another difficulty that demands attention. So saving time with AI won’t decrease the staff or trim intelligence budgets. Or maybe, the more noteworthy value of AI comes from what may be named an “automation dividend”: the better ways experts can utilize their time after these advances reduce their workload.

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