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In an economy where data is changing how companies create value and compete experts predict that using artificial intelligence (AI) at a larger scale will add as much as $15.7 trillion to the global economy by 2030. As AI is changing how companies work, many believe that who does this work will change, too and that organizations will begin to replace human employees with intelligent machines. This is already happening: intelligent systems are displacing humans in manufacturing, service delivery, recruitment, and the financial industry, consequently moving human workers towards lower-paid jobs or making them unemployed. This trend has led some to conclude that in 2040 our workforce may be totally unrecognizable.

Are humans and machine really in competition with each other though? The history of work particularly since the Industrial Revolution is the history of people outsourcing their labor to machines. While that began with rote, repetitive physical tasks like weaving, machines have evolved to the point where they can now do what we might think of as complex cognitive work, such as math equations, recognizing language and speech, and writing. Machines thus seem ready to replicate the work of our minds, and not just our bodies. In the 21st century, AI is evolving to be superior to humans in many tasks, which makes that we seem ready to outsource our intelligence to technology. With this latest trend, it seems like theres nothing that cant soon be automated, meaning that no job is safe from being offloaded to machines.

This vision of the future of work has taken the shape of a zero-sum game, in which there can only be one winner.

We believe, however, that this view of the role AI will play in the workplace is wrong. The question of whether AI will replace human workers assumes that AI and humans have the same qualities and abilities but, in reality, they dont. AI-based machines are fast, more accurate, and consistently rational, but they arent intuitive, emotional, or culturally sensitive. And, its exactly these abilities that humans posses and which make us effective.

In general, people recognize todays advanced computers as intelligent because they have the potential to learn and make decisions based on the information they take in. But while we may recognize that ability, its a decidedly different type of intelligence what we posses.

In its simplest form, AI is a computer acting and deciding in ways that seem intelligent. In line with Alan Turings philosophy, AI imitates how humans act, feel, speak, and decide. This type of intelligence is extremely useful in an organizational setting: Because of its imitating abilities, AI has the quality to identify informational patterns that optimize trends relevant to the job. In addition, contrary to humans, AI never gets physically tired and as long its fed data it will keep going.

These qualities mean that AI is perfectly suited to put at work in lower-level routine tasks that are repetitive and take place within a closed management system. In such a system, the rules of the game are clear and not influenced by external forces. Think, for example, of an assembly line where workers are not interrupted by external demands and influences like work meetings. As a case in point, the assembly line is exactly the place where Amazon placed algorithms in the role of managers to supervise human workers and even fire them. As the work is repetitive and subject to rigid procedures optimizing efficiency and productivity, AI is able to perform in more accurate ways to human supervisors.

Human abilities, however, are more expansive. Contrary to AI abilities that are only responsive to the data available, humans have the ability to imagine, anticipate, feel, and judge changing situations, which allows them to shift from short-term to long-term concerns. These abilities are unique to humans and do not require a steady flow of externally provided data to work as is the case with artificial intelligence.

In this way humans represent what we call authentic intelligence a different type of AI, if you will. This type of intelligence is needed when open systems are in place. In an open management system, the team or organization is interacting with the external environment and therefore has to deal with influences from outside. Such work setting requires the ability to anticipate and work with, for example, sudden changes and distorted information exchange, while at the same time being creative in distilling a vision and future strategy. In open systems, transformation efforts are continuously at work and effective management of that process requires authentic intelligence.

Although Artificial Intelligence (referred to as AI1 here) seems opposite to Authentic Intelligence (referred to as AI2 here), they are also complimentary. In the context of organizations, both types of intelligence offer a range of specific talents.

Which talents operationalized as abilities needed to meet performance requirements are needed to perform best? It is, first of all, important to emphasize that talent can win games, but often it will not win championships teams win championships. For this reason, we believe that it will be the combination of the talents included in both AI1 and AI2, working in tandem, that will make for the future of intelligent work. It will create the kind of intelligence that will allow for organizations to be more efficient and accurate, but at the same time also creative and pro-active. This other type of AI we call Augmented Intelligence (referred to as AI3 here).

What will AI3 be able to offer that AI1 and AI2 cant? The second author of this article has some unique insight here: he is known for winning championships, while at the same time he also has the distinctive experience of being the first human to lose a high-level game to a machine. In 1997, chess grand master Garry Kasparov lost a game from an IBM supercomputer program called Deep Blue. It left him to rethink how the intellectual game of chess could be approached differently, not simply as an individual effort but as a collaborative one. And, with the unexpected victory of Deep Blue, he decided to try collaborating with an AI.

In a match in 1998 in Len, Spain, Kasparov partnered with a PC running the chess software of his choice an arrangement called advanced chess in a match against the Bulgarian Veselin Topalov, who he had beaten 4-0 a month earlier. This time, with both players supported by computers, the match ended in a 3-3 draw. It appeared that the use of a PC nullified the calculative and strategic advances Kasparov usually displayed over his opponent.

The match provided an important illustration of how humans might work with AI. After the match, Kasparov noted that the use of a PC allowed him to focus more on strategic planning while machine took care of the calculations. Nevertheless, he also stressed that simply putting together the best human player and best PC did not, in his eyes, reveal games that were perfect. Like with human teams, the power of working with an AI comes from how the person and computer compliment each other; the best players and most powerful AIs partnering up dont necessarily produce the best results.

Once again, the chess world offers a useful test case for how this collaboration can play out. In 2005 the online chess playing site Playchess.com hosted what it called a freestyle chess tournament in which anyone could compete in teams with other players or computers. What made this competition interesting is that several groups of grandmasters working with computers also participated in this tournament. Predictably, most people expected that one of these grandmasters in combination with a supercomputer would dominate this competition but thats not what happened. The tournament was won by a pair of amateur American chess players using three computers. It was their ability to coordinate and coach effectively their computers that defeated the combination of a smart grandmaster and a PC with great computational power.

This surprising result underscores an important lesson: the process of how players and computers interact determines how efficient the partnership will be. Or, as Kasparov expressed it, Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

The enhancing and collaborative potential that we envision stands in stark contrast to the zero-sum predictions of what AI will do to our society and organizations. Instead, we believe that greater productivity and the automation of cognitively routine work is a boon, not a threat. After all, new technology always has disruptive effects early on in the implementation and development phases and usually reveals its real value only after some time.

This reality, however, does not mean that we have to wait patiently until when this value eventually reveals itself very much the opposite! Our principal challenge as business people is to anticipate what artificial intelligence means in relationship to how humans think and act, and work to integrate the new technologies ambitiously and strategically into our organizations. We cant just passively wait for it to overtake traditional methods. So, what is it that we can then do at this moment to ensure integration of the different AIs to make our organizations work effectively?

First, teams will gradually become composed of humans and non-humans working together, which we refer to as the new diversity. The psychology of the new diversity will bring with it the risk that stereotypical beliefs and biases can easily influence decisions and team work. Machine as a non-human co-worker may be met with distrust and negative expectations as any other out-group member and as such encourage humans to share less information and avoid working with machine. Team leaders will need to be apt to respond to such negative team dynamics and trained in ways that they understand the reality of those negative beliefs and its consequences.

Second, the new shape of teams will call for leaders who are skilled in bringing different parties together. In the future, creating inclusive teams by aligning man and machine will be an important ability to be trained and developed. As the earlier mentioned examples show, to achieve better performance by employing these new diversity teams, a main requirement for leaders will be to transform themselves in being masters of coordinating and coaching team processes.

Third, team processes will need to be managed effectively and this will have to be done by a human. For humans to align the strengths and weaknesses of man and machine, they will need to be educated to understand how AI works, what it can be used for and decide by means of the judgment abilities of their authentic intelligence how it can be used best to foster performance serving human interests.

Augmented intelligence, as the third type of AI, is the step forward to the future of intelligent work. The future of work is a concept used to indicate the growth of employees and their performance in more efficient ways. The debate on this topic, however, has become quite ambiguous in its intentions. Specifically, because of cost-cutting strategies narratives, businesses today are in a stage where machines are often introduced as the new super employee that may leave humans ultimately in an inferior role to serve machine. An essential element of a truly intelligent type of future of work, however, means that we do expand the workforce where both humans and machine will be part of, but with the aim to improve humanity and well-being while also being more efficient in the execution of our jobs. So, augmented intelligence is indeed collaborative in nature, but its also clear that it represents a collaborative effort in service of humans.

Excerpt from:
AI Should Augment Human Intelligence, Not Replace It hbr.org - Harvard Business Review

5 Trends in AI 2021

Artificial Intelligence innovation continues apace - with explosive growth in virtually all industries. So what did the last year bring, and what can we expect from AI in 2021?

In this article, I list five trends that I saw developing in 2020 that I expect will be even more dominant in 2021.

MLOps

MLOps (Machine Learning Operations, the practice of production Machine Learning) has been around for some time. During 2020, however, COVID-19 brought a new appreciation for the need to monitor and manage production Machine Learning instances. The massive change to operational workflows, inventory management, traffic patterns, etc. caused many AIs to behave unexpectedly. This is known in the MLOps world as Drift - when incoming data does not match what the AI was trained to expect. While drift and other challenges of production ML were known to companies that have deployed ML in production before, the changes caused by COVID caused a much broader appreciation for the need for MLOps. Similarly, as privacy regulations such as the CCPA take hold, companies that operate on customer data have an increased need for governance and risk management. Finally, the first MLOps community gathering - the Operational ML Conference - which started in 2019, also saw a significant growth of ideas, experiences, and breadth of participation in 2020.

Low Code/No Code

AutoML (automated machine learning) has been around for some time. AutoML has traditionally focused on algorithmic selection and finding the best Machine Learning or Deep Learning solution for a particular dataset. Last year saw growth in the Low-Code/No-Code movement across the board, from applications to targeted vertical AI solutions for businesses. While AutoML enabled building high-quality AI models without in-depth Data Science knowledge, modern Low-Code/No-Code platforms enable building entire production-grade AI-powered applications without deep programming knowledge.

Advanced Pre-trained Language Models

The last few years have brought substantial advances to the Natural Language Processing space, the greatest of which may be Transformers and Attention, a common application of which is BERT (Bidirectional Encoder Representations with Transformers). These models are extremely powerful and have revolutionized language translation, comprehension, summarization, and more. However, these models are extremely expensive and time-consuming to train. The good news is that pre-trained models (and sometimes APIs that allow direct access to them) can spawn a new generation of effective and extremely easy-to-build AI services. One of the largest examples of an advanced model accessible via API is GPT-3 - which has been demonstrated for use cases ranging from writing code to writing poetry.

Synthetic Content Generation (and its cousin, the Deep Fake)

NLP is not the only AI area to see substantial algorithmic innovation. Generative Adversarial Networks (GANs) have also seen innovation, demonstrating remarkable feats in creating art and fake images. Similar to transformers, GANs have also been complex to train and tune as they require large training sets. However, innovations have dramatically reduced the data sizes of creating a GAN. For example, Nvidia has demonstrated a new augmented method for GAN training that requires much less data than its predecessors. This innovation can spawn the use of GANs in everything from medical applications such as synthetic cancer histology images, to even more deep fakes.

AI for Kids

As low-code tools become prevalent, the age at which young people can build AIs is decreasing. It is now possible for an elementary or middle school student to build their own AI to do anything from classifying text to images. High Schools in the United States are starting to teach AI, with Middle Schools looking to follow. As an example - in Silicon Valleys Synopsys Science Fair 2020, 31% of the winning software projects used AI in their innovation. Even more impressively, 27% of these AIs were built by students in grades 6-8. An example winner, who went on to the national Broadcom MASTERS, was an eighth-grader who created a Convolutional Neural Network to detect Diabetic Retinopathy from eye scans.

What does all this mean?

These are not the only trends in AI. However, they are noteworthy because they point in three significant and critical directions

Original post:
Artificial Intelligence In 2021: Five Trends You May (or May Not) Expect - Forbes

CHICAGO, March 15, 2021 /PRNewswire/ -- According to the new market research report "Artificial Intelligence in Military Marketby Offering (Software, Hardware, Services), Technology (Machine Learning, Computer vision), Application, Installation Type, Platform, Region - Global Forecast to 2025",published by MarketsandMarkets,the Artificial Intelligence in Military Marketis estimated at USD 6.3 billion in 2020 and is projected to reach USD 11.6 billion by 2025, at a CAGR of 13.1% during the forecast period. An increase in funding from military research agencies and a rise in R&D activities to develop advanced AI systems are projected to drive the increased adoption of AI systems in the military sector.

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Artificial Intelligence (AI) is becoming a critical part of modern warfare as it can handle massive amounts of military data in a more efficient manner as compared to conventional systems. It improves the self-control, self-regulation, and self-actuation abilities of combat systems using inherent computing and decision-making capabilities. Some industry experts have noted that the COVID-19 pandemic has not affected the demand for Ai in Military, especially for military end use. Companies such as Lockheed Martin Corporation (US), Northrop Grumman Corporation (US), BAE Systems (UK), Rafael Advanced Defense Systems (Israel) and Thales Group (France) received contracts for the supply of AI systems to the armed forces of various nations in the first half of 2020, showcasing continuous demand during the COVID-19 crisis.

Even though the COVID-19 pandemic has caused a large-scale impact on economies across the world, leading to many challenges, the AI in military market has continued to expand. This can be seen from both, the demand and supply sides, as leading manufacturers like Lockheed Martin (US), IBM (US), Northrop Grumman (US), and others continue to invest heavily in developing AI capabilities, and governments continue to invest significantly in securing these systems. This can be attributed to governments realizing the potential of improved capabilities that these AI systems offer in terms of defense arsenal as the global AI arms race tightens.

However, even though the development of AI technology witnessed expansion, the overall building of the AI systems saw a hit. This was a result of the shortage of raw materials due to disruptions in the supply chain. Resuming manufacturing and demand depends on the level of COVID-19 exposure a country is facing, the level at which manufacturing operations are running, and import-export regulations, among other factors. Although companies may still be taking in orders, delivery schedules might not be fixed.

Increasing Threats of Cyber Attacks is driving the growth of the defense applications that leverages AI

The defense industry across countries is constantly under threat of cyberattacks. For instance, in September 2019, SolarWinds, a US technology company, was hacked, revealing sensitive data of many hospitals, universities, and US government agencies. Another notable incident was in October 2020, when the FBI and the US Cyber Command announced that a North Korean group had hacked think tanks, individual experts, and government entities of the US, Japan, and South Korea to illegally obtain intelligence, including that on nuclear policies.

Current cybersecurity technology falls short in terms of tackling advanced ransomware and spyware threats. The above mentioned SolarWinds hack was revealed when FireEye, a cybersecurity provider, was probing one of its own hacks. Such incidents indicate the increasing importance of having advanced cybersecurity capabilities. Artificial intelligence-based cybersecurity solutions that can be trained to independently gather data from various sources, analyze the data, correlate it to the signals indicating cyberattacks, and take relevant actions, can be deployed.

Browsein-depth TOC on"Artificial Intelligence in Military Market"

164 Tables 77 Figures278 Pages

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Based on platform, the space segment of theArtificial Intelligencein military market is projected to grow at the highest CAGR during the forecast period

Based on platform, the space segment of the Artificial Intelligence in military market is projected to grow at the highest CAGR during the forecast period. The space AI segment comprises CubeSat and satellites. Artificial intelligence systems for space platforms include various satellite subsystems that form the backbone of different communication systems. The integration of AI with space platforms facilitates effective communication between spacecraft and ground stations.

Software segment of theArtificial Intelligencein Military market by offering is projected to witness the highest CAGR during the forecast period

Based on offering, the Software segment is projected to witness the highest CAGR during the forecast period. Technological advances in the field of AI have resulted in the development of advanced AI software and related software development kits. AI software incorporated in computer systems is responsible for carrying out complex operations. It synthesizes the data received from hardware systems and processes it in an AI system to generate an intelligent response. Software segment is projected to witness the highest CAGR owing to the significance of AI software in strengthening the IT framework to prevent incidents of a security breach.

The North America market is projected to contribute the largest share from 2020 to 2025 in theArtificial Intelligencein Military market

The US and Canada are key countries considered for market analysis in the North American region. This region is expected to lead the market from 2020 to 2025, owing to increased investments in AI technologies by countries in this region. This market is led by the US, which is increasingly investing in AI systems to maintain its combat superiority and overcome the risk of potential threats on computer networks. The US plans to increase its spending on AI in military to gain a competitive edge over other countries.

The North America US is recognized as one of the key manufacturers, exporters, and users of AI systems worldwide and is known to have the strongest AI capabilities. Key manufacturers of Ai systems in the US include Lockheed Martin, Northrop Grumman, L3Harris Technologies, Inc., and Raytheon. The new defense strategy of the US indicates an increase in Ai spending to include advanced capabilities in existing defense systems of the US Army to counter incoming threats.

Related Reports:

Military Embedded Systems Marketby Component (Hardware, Software), Server Architecture (Blade Server, Rack-Mount Server), Platform (Land, Airborne, Naval, Space), Installation (New Installation, Upgradation), Application, Services, and Region - Global Forecast to 2025.

Network Centric Warfare (NCW) Marketby Platform (Land, Air, Naval, Unmanned), Application (ISR, Communication, Computer, Cyber, Combat, Control & Command), Mission Type, Communication Network, Architecture, and Region - Global Forecast to 2021

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Artificial Intelligence in Military Market worth $11.6 billion by 2025 - Exclusive Report by MarketsandMarkets - PRNewswire

Since 2019 the FDAs crosscutting work has implemented artificial intelligence (AI) as part of the its New Era of Smarter Food Safety initiative. This new application of available data sources can strengthen the agencys public health mission with the goal using AI to improve capabilities to quickly and efficiently identify products that may pose a threat to public health by impeding their entry into the U.S. market.

On February 8 the FDA reported the initiation of their succeeding phase for AI activity with the Imported Seafood Pilot program. Running from February 1 through July 31, 2021, the pilot will allow FDA to study and evaluate the utility of AI in support of import targeting, ultimately assisting with the implementation of an AI model to target high-risk seafood productsa critical strategy, as the United States imports nearly 94% of its seafood, according to the FDA.

Where in the past, reliance on human intervention and/or trend analysis drove scrutiny of seafood shipments such as field exams, label exams or laboratory analysis of samples, with the use of AI technologies, FDA surveillance and regulatory efforts might be improved. The use of Artificial intelligence will allow for processing large amount of data at a faster rate and accuracy giving the capability for revamping FDA regulatory compliance and facilitate importers knowledge of compliance carrying through correct activity. FDA compliance officers would also get actionable insights faster, ensuring that operations can keep up with emerging compliance requirements.

Predictive Risk-based Evaluation for Dynamic Imports Compliance (PREDICT) is the current electronic tracking system that FDA uses to evaluate risk using a database screening system. It combs through every distribution line of imported food and ranks risk based on human inputs of historical data classifying foods as higher or lower risk. Higher-risk foods get more scrutiny at ports of entry. It is worth noting that AI is not intended to replace those noticeable PREDICT trends, but rather augment them. AI will be part of a wider toolset for regulators who want to figure out how and why certain trends happen so that they can make informed decisions.

AIs focus in this regard is to strengthen food safety through the use of machine learning and identification of complex patterns in large data sets to order to detect and predict risk. AI combined with PREDICT has the potential to be the tool that expedites the clearance of lower risk seafood shipments, and identifies those that are higher risk.

The unleashing of data through this sophisticated mechanism can expedite sample collection, review and analysis with a focus on prevention and action-oriented information.

American consumers want safe food, whether it is domestically produced or imported from abroad. FDA needs to transform its computing and technology infrastructure to close the gap between rapid advances in product and process technology solutions to ensure that advances translate into meaningful results for these consumers.

There is a lot we humans can learn from data generated by machine learning and because of that learning curve, FDA is not expecting to see a reduction of FDA import enforcement action during the pilot program. Inputs will need to be adjusted, as well as performance and targets for violative seafood shipments, and the building of smart machines capable of performing tasks that typically require human interaction, optimizing workplans, planning and logistics will be prioritized.

In the future, AI will assist FDA in making regulatory decisions about which facilities must be inspected, what foods are most likely to make people sick, and other risk prioritization factors. As times and technologies change, FDA is changing with them, but its objective remains in protecting public health. There is much promise in AI, but developing a food safety algorithm takes time. FDAs pilot program focusing on AIs capabilities to strengthen the safety of U.S. seafood imports is a strong next step in predictive analytics in support of FDAs New Era of Smarter Food Safety.

Read the rest here:
Regulatory Cross Cutting with Artificial Intelligence and Imported Seafood | FoodSafetyTech - FoodSafetyTech

Artificial Intelligence has changed our lives for better. Be it in the form of robots, automated cars, or voice based applications like Alexa and Siri, we have seen it all. Without a doubt, AI is that one technology that makes the best use of human intelligence to take up tasks that earlier could only be performed by humans. Machines now stand the potential to learn and put the knowledge gained in the best possible use. All the human-like tasks are now performed using AI.

There are several aspects to Artificial Intelligence and so are the fields within this splendid technology. Some of them that have successfully garnered attention and appreciation equally from every corner of the world are natural language processing (NLP), computer vision, and deep learning. Machine learning is that sub field of deep learning that mainly revolves around analysing data and making predictions out of the analysed data. Needless to say, all this relies heavily on human supervision.

SMU Assistant Professor of Information Systems, Sun Qianru, talks about how training an Artificial Intelligence model has so much in similarity to that of how parents teach their child to identify objects.

Considering the complexity that Artificial I is associated with, Professor Suns research mainly talks about

Meta learning

Semi-supervised learning

Deep convolutional neural networks

Incremental learning

Well, not just that. The research also revolves around the application of all of these in recognizing images and videos.

The research, Fast-Adapted Neural Networks (FANN) for Advanced AI Systems is currently in its early stage. The research revolves around computer vision. This aspect of computer vision employs algorithms that rely on CNNs (Convolutional neural networks). The areas under scrutiny are image recognition, image processing, etc. All of this work is funded by the Agency for Science, Technology and Research (A*STAR).

Building the reasoning level ofmodeladaptation based on statistical-level knowledge learning is the hypothesis of FANN. Heres everything that the research talks about

Knowing the fact as to how complex AI is, Suns research talks about how critical it is to train AI model that is in line with the current trends in the field.

When a model is trained to yield accurate recognition results, the amount of data that goes in is immense. Sun cites an example of face recognition to support this. She argues that if theres just one face available for the system to recognize, then how will it be possible for it to differentiate that one face from the rest? Only when adequate amount of data comes into play, only when other faces too are employed for face recognition should the model be successful in distinguishing. To learn the differences, the model should have huge data that it can rely on.

All said and noted, the fact that machine learning models stand the potential to identify the global features cannot be overlooked. These models encode the data available that help in producing desired identification results. The models are successful in recognizing from images, text or sound. All of these employ deep neural network architectures that contain many layers.

Suns research takes into account two main aspects. One is where some machine learning models train themselves on a labelled data set. The other being how the best performing AI models are all based on deep learning. The research addresses the point how models are built to determine the data followed by classifying it.

The professor talks about how some models get updated when the prediction made turns out to be wrong.

Theres yet another project that Sun is working on. It is a food related application for the Health Promotion Board based out of Singapore. The main idea behind this app is to enable the users have a fair knowledge about the nutritional values of the food that they consume. The users can make use of this information to lead a healthy lifestyle. All that the users have to do is take pictures of the food theyre consuming and thats it. All the relevant information is out there on their smartphones.

However, this is where the complexity began. While training a model, her team had introduced a limited set of categories into it. But, with different photos being clicked, the need of expanding the categories came into play. Not just this, the category list was required to be updated and modified in the Application Programming Interface (API) on a regular basis.

The rich diversity that the place brings in posed a challenge for the team. With a different place, comes in a different culture. Hence, the team needs to pay extra attention to train their models by employing effective learning algorithms.

All this calls for not only diverse data collection but also on developing different adaptation learning algorithms. The complexity is for sure in existence and the team plans to deal with this by making use of a small data set.

This research by Sun and her team aims to achieve high robustness and computational efficiency, especially in the image recognition aspect. The research team is confident the outcomes of the research will have tons of benefits to offer. The key ones being great improvement in the yield rate and reduction in the manufacturing costs. All this would play a pivotal role when the fast-adapted inspection devices undergoes the process of installation, fabrication, and testing.

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Artificial Intelligence and its Complexity: Breaking the Ice - Analytics Insight