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DENVER--(BUSINESS WIRE)--Palantir Technologies Inc. (NYSE: PLTR) today announced that it will expand its work with the U.S. Army Research Laboratory to implement data and artificial intelligence (AI)/machine learning (ML) capabilities for users across the combatant commands (COCOMs). The contract totals $99.9 million over two years.

Palantir first partnered with the Army Research Lab to provide those on the frontlines with state-of-the-art operational data and AI capabilities in 2018. Palantirs platform has supported the integration, management, and deployment of relevant data and AI model training to all of the Armed Services, COCOMs, and special operators. This extension grows Palantirs operational RDT&E work to more users globally.

Maintaining a leading edge through technology is foundational to our mission and partnership with the Army Research Laboratory, said Akash Jain, President of Palantir USG. Our nations armed forces require best-in-class software to fulfill their missions today while rapidly iterating on the capabilities they will need for tomorrows fight. We are honored to support this critical work by teaming up to deliver the most advanced operational AI capabilities available with dozens of commercial and public sector partners.

By working with the U.S. Army Research Lab, integrating with partner vendors, and iterating with users on the front lines, Palantirs software platforms will continue to quickly implement advanced AI capabilities against some of DODs most pressing problem sets. Were looking forward to fielding our newest ML, Edge, and Space technologies alongside our U.S. military partners, said Shannon Clark, Senior Vice President of Innovation, Federal. These technologies will enable operators in the field to leverage AI insights to make decisions across many fused domains. From outer space to the sea floor, and everything in between.

About Palantir Technologies Inc.

Foundational software of tomorrow. Delivered today. Additional information is available at https://www.palantir.com.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements may relate to, but are not limited to, Palantirs expectations regarding the amount and the terms of the contract and the expected benefits of our software platforms. Forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified. Forward-looking statements are based on information available at the time those statements are made and were based on current expectations as well as the beliefs and assumptions of management as of that time with respect to future events. These statements are subject to risks and uncertainties, many of which involve factors or circumstances that are beyond our control. These risks and uncertainties include our ability to meet the unique needs of our customer; the failure of our platforms to satisfy our customer or perform as desired; the frequency or severity of any software and implementation errors; our platforms reliability; and our customers ability to modify or terminate the contract. Additional information regarding these and other risks and uncertainties is included in the filings we make with the Securities and Exchange Commission from time to time. Except as required by law, we do not undertake any obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future developments, or otherwise.

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U.S. Army Research Lab Expands Artificial Intelligence and Machine Learning Contract with Palantir for $99.9M - Business Wire

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DUBLIN--(BUSINESS WIRE)--The "Brazil Artificial Intelligence in Commercial Airline Market: Prospects, Trends Analysis, Market Size and Forecasts up to 2027" report has been added to ResearchAndMarkets.com's offering.

The country research report on Brazil artificial intelligence in commercial airline market is a customer intelligence and competitive study of the Brazil market. Moreover, the report provides deep insights into demand forecasts, market trends, and, micro and macro indicators in the Brazil market.

Also, factors that are driving and restraining the artificial intelligence in commercial airline market are highlighted in the study. This is an in-depth business intelligence report based on qualitative and quantitative parameters of the market. Additionally, this report provides readers with market insights and detailed analysis of market segments to possible micro levels. The companies and dealers/distributors profiled in the report include manufacturers & suppliers of artificial intelligence in commercial airline market in Brazil.

Segments Covered

Segmentation Based on Offering

Segmentation Based on Solution

Segmentation Based on Technology

Highlights of the Report

The report provides detailed insights into:

1) Demand and supply conditions of artificial intelligence in commercial airline market

2) Factor affecting the artificial intelligence in commercial airline market in the short run and the long run

3) The dynamics including drivers, restraints, opportunities, political, socioeconomic factors, and technological factors

4) Key trends and future prospects

5) Leading companies operating in artificial intelligence in commercial airline market and their competitive position in Brazil

6) The dealers/distributors profiles provide basic information of top 10 dealers & distributors operating in (Brazil) artificial intelligence in commercial airline market

7) Matrix: to position the product types

8) Market estimates up to 2027

Key Topics Covered:

1. Report Overview

2. Executive Summary

3. Market Overview

4. Brazil Artificial Intelligence in Commercial Airline Market by Offering

5. Brazil Artificial Intelligence in Commercial Airline Market by Solution

6. Brazil Artificial Intelligence in Commercial Airline Market by Technology

7. Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/evnpvr

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Brazil Artificial Intelligence in Commercial Airline Market Report 2022: Key Trends, Players and Drivers - ResearchAndMarkets.com - Business Wire

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AI for cybersecurity; its everywhere else!

You can use artificial intelligence (AI) to automate complex repetitive tasks much faster than a human. AI technology can sort complex, repetitive input logically. Thats why AI is used for facial recognition and self-driving cars. But this ability also paved the way for AI cybersecurity. This is especially helpful in assessing threats in complex organizations. When business structures are continually changing, admins cant identify weaknesses traditionally.

Additionally, businesses are becoming more complex in network structure. This means cybercriminals have more exploits to use against you. You can see this in highly automated manufacturing 3.0 businesses or integrated companies like the oil and gas industry. To this end, various security companies have developed AI cybersecurity tools to help protect businesses.

In this article, Ill delve into what AI is and how it applies to cybersecurity. Youll also learn the benefits and drawbacks of this promising technology. First, lets take a look at what AI is!

Artificial intelligence is a rationalization method using a statistically weighted matrix. This matrix is also called a neural net. You can think of this net as a decision matrix with nodes that have a weighted bias for each filtering process. The neural net will receive a database of precompiled data. This data will also contain answers to the underlying question the AI solves. This way, the AI will create a bias.

For example, lets consider a database containing different images. Lets say it has images of a persons face and other images of watermelons. Additionally, each image has a tag to check each item. As the AI learns whether it guessed correctly or not, the system increments node weightings. This process continues until the system reaches a predefined error percentage. This is often referred to as deep learning, which refers to the decision layers creating the depth.

Now, lets take a look at the steps used to process data.

We can condense the overall data workflow into the following process:

However, this process is slightly different with deep learning. The first step would include data from a precompiled database tagged with the correct response. Additionally, deep learning will repeat steps 1 through 4 to reach a predefined error tolerance value.

Lets take a look at this with an example of how AI data is processed.

Lets say a picture has reached an AI node. The node will filter the data into a usable format like 255 grayscale. Then, itll run a script to identify features, for example. If these features match others from a filter, the node can make a decision. For instance, itll say whether it found a face or a watermelon.

Then, the data goes to the next node down. This specific node could have a color filter to confirm the first decision. The process continues until the data reaches the last node. At that point, the AI will have made a final decision, ensuring whether it found a face or a watermelon.

Importantly, AI systems will always have a degree of error to them. None are infallible, and they never will be. But sometimes, the error percentages could be acceptable.

Now that you know how AI works lets take a look at AI cybersecurity solutions.

AI cybersecurity addresses the need to automate the assessment of threats in complex environments. Specifically, here are 2 use-cases for AI in AI cybersecurity:

Now you know the two main uses of AI in cybersecurity, lets take a look at its benefits and drawbacks!

As mentioned, AI has a lot of benefits. It runs repetitive tasks to identify anomalies or to classify data in particular in your business. That said, a few large drawbacks may offset its benefits. Here, well look at the drawbacks.

The first drawback is the AI cybersecurity solutions accuracy. This accuracy also depends on many factors. This includes the neural nets size and the decisions defined for filtering. It also depends on the number of iterations used to reach the predefined error percentage.

Imagine you have a decision tree with three layers. And each layer has several nodes for each decision route. Even though this is a fairly simple matrix, it needs a lot of calculations. Your systems finite resources will compromise your solutions intelligence.

An AI cybersecurity solution provider may stunt its solutions intelligence/accuracy to meet the target demographic. But sometimes, the problem isnt intelligence. Instead, its low latency and security vulnerabilities. When searching for an AI cybersecurity solution, consider how secure it is in your network.

Once trained, an AI statistical weighted matrix is often not re-trained in service. Youll find this is due to the lack of processing resources available in hardware. Sometimes, the system learns something that makes it worse, reducing effectiveness. Conversely, humans learn iteratively. This means they cause a lot of accidents. As a result, solution providers must ensure the software meets specification requirements during use.

Cybersecurity often requires updates to counter new exploits. To this end, it takes a lot of power to train your AI. Additionally, your AI cybersecurity vendor will need to update regularly to address cyber threats.

That said, the AI component of an AI cybersecurity solution is for classifying data and assessing anomalies in baseline data. As a result, it doesnt cause an issue for malware list updates. This means you can still use AI cybersecurity.

Now you know the benefits and drawbacks of AI cybersecurity, lets take a look at some uses for this technology!

As mentioned, highly automated businesses have the weakest cybersecurity. Generally, automated environments will overlap information technology (IT), operational technology (OT), and the Internet of things (IoT). This is to improve productivity, reduce the unit cost of a product, and undercut the competition.

But this also creates vulnerabilities. To this end, AI cybersecurity is great for finding potential exploits in these companies. Solutions either inform the administrator or automatically apply patches.

However, this may not be enough. Cybercriminals are currently attacking large, highly integrated companies. To do that, they exploit OT, which has no security. This OT was meant for wired networks to send commands to hardware like plant equipment. This means it never posed a security weakness. But today, attackers use OT to access the rest of a network or take plant equipment offline.

OT risk management tools are becoming popular for the reasons mentioned above. These systems effectively take a real-time clone of the production environment. Then, they run countless simulations to find exploits.

The AI part of the system generally finds exploits. In that case, an administrator provides a solution. OT risk management software continually runs as manufacturing plant arrangements change to meet orders, projects, or supply demands.

In this scenario, AI systems use known malware from antivirus lists to try and find an entry route into the system. The task requires automated repetitive functions of a complex system. And this makes it perfect for AI

So when should you implement AI cybersecurity? Lets find out.

As discussed above, businesses that use manufacturing and plant equipment should use AI cybersecurity. In most cases, youll also need to look for an OT risk management solution to reduce risks associated with OT.

You also can use AI cybersecurity if your business uses IoT and IT. This way, you can reduce the risk to the network from exploits. IoT devices generally undercut competitors, so you bypass the cost of adding adequate security measures.

Finally, you can use AI even if your company only uses IT. AI helps assess irregular traffic, so it protects your gateways. Additionally, you can leverage AIs data analytics. This way, youll know if someone is using your hardware for malicious purposes.

Now you know all you need to get started with AI cybersecurity, lets wrap things up!

Youll likely use AI wherever you need automated repetitive tasks. AI also helps make decisions on complex tasks. This is why many cybersecurity solution providers use AI. In fact, these providers tools help meet the challenge of highly complex systems that have very poor security.

You can always benefit from AI cybersecurity. It doesnt matter how integrated your business technology is. AI functionality is also great for classifying data using intelligent operations. This way, you can speed up your search for malware. AI cybersecurity is also beneficial for finding abnormal use of the network.

Do you have more questions about AI cybersecurity? Check out the FAQ and Resources sections below!

An AI neural net is a statical weighted matrix. This matrix helps process input data based on decisions made at nodes with a calibrated bias. To optimize this bias, data gets iteratively passed through the matrix. After that, the success rate is assessed, and each weighting value brings incremental changes. This process is called deep learning.

AI intelligence refers to the AIs error tolerance and decision layers. In theory, you could have as many layers as needed to make an intelligent AI. However, training it with data to reach a high error tolerance could be processor-intensive. This training may also take too long to produce. As a result, the solution becomes ineffective.

AI is trained using data to meet a predefined error tolerance level. For instance, a self-driving car lasts 1,000,000 miles by design. In this case, the cars service life determines the AI error tolerance. The AI accuracy must likely be 99.99% correct during decision-making to meet the service life

Operations technology (OT) risk assessment software assesses the security risks of plant equipment. Plants, integrated oil supply chains, and manufacturing 3.0 or above are also prime targets for attacks. AI cybersecurity can help assess threats using a clone of the production system. This helps check routes from OT systems to the rest of the system.

Yes, AI cybersecurity works in real-time. This helps detect weaknesses in your network or cyber threats. For example, you can find weaknesses by assessing traffic data through gateways and other hardware. You also can use AI as a centralized OT risk assessment software. This will let you assess the network structure for threats.

Learn about the different types of malware your AI cybersecurity solution will have to deal with.

Find out more about AI cybersecurity.

Discover more about AI and deep learning.

Understand how you can protect your organization by following GRC.

Learn how you can make your OPSEC better.

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Artificial Intelligence in Cyber Security: Benefits and Drawbacks. - TechGenix

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DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence Computing Software: Market Analysis" report has been added to ResearchAndMarkets.com's offering.

Market is predicted to grow from $ 6.9B in 2021 to $ 37.6B in 2026 and may become a new sector of the economy.

This research contains complete information of the AI-related processors specifications and capabilities which were produced by the key market players and start-ups.

This comprehensive analysis can aid you in your technology acquisitions or investment decisions related to the fast-growing AI processors market.

After the main breakthrough at the turn of the century AI started to incorporate more and more artificial neural networks, connected in an ever-growing number of layers, now known as Deep Learning (DL). They can compete and outperform classical ML techniques like clustering but are more flexible and can work with much more complex datasets, including images and audio.

As machine learning entered exponential growth, it expanded into areas usually dominated by high-performance computing - such as protein folding and many-particle interactions. At the same time, our lives become increasingly dependent on its availability and reliability. This poses a number of new technical challenges but at the same time opens a road to novel solutions and technologies, in a similar way as space exploration or fundamental physics does.

More so, the commercial success of AI-enabled systems (autopilots, image processing, speech recognition and translation, to name just a few) ensures that no shortage of funds could hinder this growth. It has clearly become a new industry, if not a sector of the economy, one that is gaining importance with every passing year.

As any industry, it depends on several factors to prosper. Rising consumer demand has led to the consensus of major forecasters on the rapid growth of the sector - around 40% yearly in the near future, so funds shortage is not an issue. Instead, we must concentrate on other requirements for the efficient functioning of the industry.

The three main components are the availability of processing tools, the abundance of raw materials, and the workforce. Raw materials in this case are represented by big data, and there is often more of it than our current systems can make sense of. The workforce also seems to grow sufficiently fast, as ML cements its place in the university curriculum. So the processing tools, as well as the available energy to run them are clear bottlenecks in the exponential growth.

The end of Moore's extrapolation law due to quantum tunnelling and such, which become increasingly important with the reduction in transistor size, sets clear bounds on where we can go. To ensure long-term investments in the industry, a clear strategy must be developed to offset what will happen in 10 years

Key Highlights

Key Topics Covered:

1. Deep learning challenges

1.1 Architectural limitations

1.2 Brief introduction to deep learning

1.3 Cutting corners

1.4 Processing tools

2. Market analysis

2.1 Market overview

2.2 CPU

2.3 Edge and Mobile

2.4 GPU

2.5 FPGA

2.6 ASIC

2.6.1 Tech giants

2.6.2 Startups

2.7 Neuromorphic processors

2.8 Photonic computing

3. Glossary

4. Infographics

For more information about this report visit https://www.researchandmarkets.com/r/5wsx87

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Artificial Intelligence Computing Software Market Analysis Report 2022: Complete Information of the AI-related Processors Specifications and...

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