Algorithmia, a provider of enterprise machine learning operations (MLOps) software, has been selected by Raytheon Intelligence & Space, a Raytheon Technologies (NYSE: RTX) business to support the team’s development of the U.S. Army’s Tactical Intelligence Targeting Access Node (TITAN) program. TITAN is a tactical ground station that finds and tracks threats to support long-range precision targeting.

Algorithmia, along with other leaders in artificial intelligence and machine learning, will enable Raytheon Technologies TITAN team to deliver easily digestible data to Army operators. TITAN will ingest data from space and high-altitude, aerial and terrestrial sensors to provide targetable data to defense systems. It also provides multi-source intelligence support to targeting, and situational awareness and understanding for commanders.

Algorithmia’s MLOps platform has been used by over 130,000 data scientists in a wide range of organizations. Its customers include large and midsize enterprises, Fortune 500 companies, the United Nations and multiple government intelligence agencies. The company’s momentum is a product of growing interest in AI-based applications and the need organizations have to efficiently manage cost and security for machine learning models.

“Machine learning significantly accelerates the process by which organizations can uncover important data points and respond to critical issues,” said Diego Oppenheimer, CEO of Algorithmia. “Our platform streamlines the deployment of machine learning models into production while providing important oversight, including review for ethical standards, to ensure models operate when and how they should, which makes Algorithmia a natural fit for sensitive applications. We are excited to join Raytheon in supporting its work with the U.S. Army.”

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How AI Enables Intuitive Camera Control For Drone Cinematography

When Greta Thunberg boarded a transatlantic zero-emissions yacht she garnered the attention of citizens of the world on the fact that aviation is a polluter of the environment that we continuously ignore. The giant industry is responsible for producing 915 million tonnes of carbon dioxide emissions along with other dangerous gases that cause environmental changes like cirrus clouds. These emissions constitute two percent of the world’s greenhouse emissions.

From the electrification of jets to biofuel many ideas have been suggested to make flying more eco friendly. The problem with these ideas is that they are just appetizers without main course. Most of these projects are either prototypes or in the pipeline getting developed forever. 

Now, for the first time, to address major challenges, the idea of AI in aviation was presented at the Singapore Air Show. The Singapore Aerospace Association A*STAR, hosted the Singapore Aerospace Technology leadership forum (STALF). The conference looked at two areas of emerging technologies: Artificial intelligence and digitization of AI operations. 

Air Alaska to the rescue

Air Alaska became the first aviation giant to successfully implement the technology that would revolutionize the aviation industry. With a fleet of 320 aircraft, the company produces enough carbon emissions to take note. With artificial intelligence, the company aimed to solve managerial problems.  The Artificial intelligence vendor, Airspace Intelligence visited the network centers of Alaska Airlines and observed all problems that caused the planes to consume more fuel and used them to build a development process for the airlines. Numerous trials were conducted on the planes that changed the business management of the airlines.

The company developed a 4D real-time map for the airlines. It displayed all relevant information on an easy-to-understand screen. The screen displays every information like FAA data feeds, turbulence routes, weather reports. It also allows the ATC to fly eight hours forward into time and access the data and suggest the best routes to follow for minimum fuel consumption. The programme also has built-in monitoring and predictive abilities. It continuously monitors schedules and active flights across the United States and autonomously looks over operational safety, the efficiency of flights running, and ATC compliance. It then makes recommendations that the operator is free to accept or reject. This allows the decision-makers to quickly access the air space, make decisions, and implement them. 

The benefits of the implementation of artificial intelligence were enormous. The airline saved 480,000 gallons of fuel in six months. It is an enormous quantity of fuel for a business whose major expenditure is fuel. It also reduced 4600 tons of carbon dioxide. It is a significant dent in carbon emissions considering that only one airline leveraged the technology.

In 2020 when the world was reeling from the Covid-19 pandemic, Air France slashed its greenhouse emissions by half by leveraging AI in flight operations. Instead of counterbalancing the carbon output, the airline introduced a programme that reduces emissions by optimizing data coming from the plane’s black boxes. Using artificial intelligence, the system evaluates the whole volume of data coming from the plane’s communication systems, flight plans, and data recorders. Combining this data with real-time data about the flight itself. The algorithm can generate a deep analysis of the efficiency of fuel utilization on any flight. Utilizing this data to make recommendations saved in fuel consumption and reduced emissions.

The Skybreathe programme designed by Open Airlines has other customers like Norwegian, Go Air, Malaysian Airlines, and more. The company helped these airlines save more than $150 million in fuel costs and in reducing carbon dioxide emissions by 590,000 tonnes.

Predictive maintenance and air traffic control will be revolutionized by Artificial intelligence and Machine learning. Optimizing the systems will make airline transport more efficient and environmentally friendly. It can accelerate the digital transformation in terms of optimizing trajectories, creating greener routes, and increasing prediction accuracy. 

It is a pioneering change in the aviation industry. Fuel consumption is affected by all kinds of changes from weather, speed to the altitude at which a plane is flying. By presenting the data to pilots to improve the efficiency of these flights by suggesting actionable changes. The recommendations can vary from changing flight routes due to turbulence, reducing acceleration altitude, or controlling when the engine is cut-off during touchdown. Also, it can advise on the modernization of fleets to improve operational efficiency. 

Optimizing these operations with artificial intelligence and machine learning will make air travel safer, efficient, and more environmentally friendly. It will be a milestone in the sector achieving the goals of the Paris Agreement that it has been committed to.

The post How Airlines Use AI To Streamline Operations, Save Fuel appeared first on Artificial Intelligence.

Retail data platform SoundCommerce brought in $15 million in Series A funding from Emergence Capital  to equip consumer brands with technology enabling them to provide shopper experiences that drive profitable growth.

Co-founders Eric Best and Jared Stiff started the Seattle-based company in 2018 after working together at Amazon and now touts clients such as Eddie Bauer, Lucky Brand and FTD.

“We’re making it affordable, fast and manageable to adopt modern cloud infrastructure, specifically data warehouses, like Snowflake,” CEO Best told Crunchbase News. “The second thing that we’re doing is that by bringing data together in a way that makes it actionable for these brands and retailers, we’re helping them align their business around their most strategic objectives.”

The company’s operations data platform collects data from any source and models the metrics and relationships most important to retailers, whether that is customers, orders, shipments or the front-end marketing stack, to enable brands to make better decisions, Best said.

In addition, the platform integrates with major commerce systems, including Amazon, Shopify,  Magento and Salesforce , and tracks real-time operational events, profitability and customer lifetime value to provide insights.

Market growth

In 2020, Amazon accounted for nearly one-third of e-commerce in the U.S., according to a Digital Commerce 360 report. Overall, e-commerce grew 44 percent year over year, with the online portion — boosted by the global pandemic — of retail sales accounting for nearly 22 percent, up from 15.8 percent in 2019.

“What’s interesting about this is that independent brands drove that growth in a more significant way than Amazon did,” Best said. “The expectation is that Amazon is going to start to experiment with e-commerce enablement beyond the branded storefront it operates.”

Meanwhile, the new funding gives SoundCommerce a total of $21.5 million in venture-backed funding since 2018, Best said. This includes a $6.5 million seed round in 2019, led by Defy.vc, according to Crunchbase data.

Since raising the seed round, the company has been working on building its technology and proving product market fit. SoundCommerce went after the Series A following accelerated growth and will continue to work on its technology and formalize its go-to-market strategy and product, Best said.

“We are feeling e-commerce tailwinds in the market; our annual recurring revenue grew 300 percent from 2019 to 2020, and we expect a similar growth trajectory this year,” he added. “We have a clear roadmap of the specific modules that we intend to launch. There are additional retail functions we’re building out for this year, and we’re also formalizing partnerships with larger SaaS providers and cloud providers in this market.”

What investors have to say

Joe Floyd, general partner of Emergence Capital, a cloud-focused VC firm focused on Series A, was introduced to SoundCommerce by a seed investor as a possible fit for the firm’s modern data stack thesis.

When Floyd had an initial conversation with Best, he said he felt they aligned on the same thing: the thesis and how Emergence viewed the world, and thought it was a good fit.

“Emergence was looking for somebody who had industry domain expertise, so they understood the industry, and they could use their relationships to kind of pry open those first big customers,” Floyd said in an interview. “Eric and his co-founder, Jared, had that rare combination of industry expertise and data expertise.”

Emergence Capital is an investor in Crunchbase. They have no say in our editorial process.

Salesforce Ventures is an investor in Crunchbase. They have no say in our editorial process.

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The Internet of Things (IoT) and machine learning are two of the most disruptive technologies in business today. Separately, both of these innovations can bring remarkable benefits to any company. Together, they can transform your business entirely.

The intersection of IoT devices and machine learning is a natural progression. Machine learning needs large pools of relevant data to work at its best, and the IoT can supply it. As adoption of both soars, companies should start using them in conjunction.

1. Highlighting and Addressing Inefficiencies

Around 25% of businesses today use IoT devices, and this figure will keep climbing. As companies implement more of these sensors, they add places where they can gather data. Machine learning algorithms can then analyze this data to find inefficiencies in the workplace.

Looking at various workplace data, a machine learning program could see where a company spends an unusually high amount of time. It could then suggest a new workflow that would reduce the effort employees expend in that area. Business leaders may not have ever realized this was a problem area without machine learning.

Machine learning programs are skilled at making connections between data points that humans may miss. They can also make predictions 20 times earlier than traditional tools and do so with more accuracy. With IoT devices feeding them more data, they’ll only become faster and more accurate.

2. Business Process Automation

Machine learning and the IoT can also automate routine tasks. Business process automation (BPA) leverages AI to handle a range of administrative tasks, so workers don’t have to. As IoT devices feed more data into these programs, they become even more effective.

Over time, technology like this has contributed to a 40% productivity increase in some industries. Automating and streamlining tasks like scheduling and record-keeping frees employees to focus on other, value-adding work. BPA’s potential doesn’t stop there, either.

BPA can automate more than straightforward data manipulation tasks. It can talk to customers, plan and schedule events, run marketing campaigns and more. With more comprehensive IoT implementation, it would have access to more areas, becoming even more versatile.

3. Supply Chain Visibility

One of the most promising areas for IoT implementation is in the supply chain. IoT sensors in vehicles or shipping containers can provide companies with critical information like real-time location data or product quality. This data alone improves supply chain visibility, but paired with machine learning, it could transform your business.

Machine learning programs can take this real-time data from IoT sensors and put it into action. It could predict possible disruptions and warn workers so they can respond accordingly. These predictive analytics could save companies the all-too-familiar headache of supply chain delays.

UPS’ Orion tool is the gold standard for what machine learning can do for supply chains. The system has saved the shipping giant 10 million gallons of fuel a year by adjusting routes on the fly based on traffic and weather data.

4. Risk Management

If a company can’t understand the vulnerabilities it faces, business leaders can’t make fully informed decisions. IoT devices can provide the data businesses need to get a better understanding of these risks. Machine learning can take it a step further and find points of concern in this data that humans could miss.

IoT devices can gather data about the workplace or customers that machine learning programs then process. For example, Progressive has made more than 1.7 trillion observations about its customers’ driving habits through Snapshot, an IoT tracking device. These analytics help the company adjust clients’ insurance rates based on the dangers their driving presents.

Business risks aren’t the only hazards the Internet of Things and machine learning can predict. IoT air quality sensors could alert businesses when to change HVAC filters to protect employee health. Similarly, machine learning cybersecurity programs could sense when hackers are trying to infiltrate a company’s network.

5. Waste Reduction

Another way the IoT and machine learning could transform your business is by eliminating waste. Data from IoT sensors can reveal where the company could be using more resources than it needs. Machine learning algorithms can then analyze this data to suggest ways to improve.

One of the most common culprits of waste in businesses is energy. Thanks to various inefficiencies, 68% of power in America ends up wasted. IoT sensors can measure where this waste is happening, and with machine learning, adjust to stop it.

Machine learning algorithms in conjunction with IoT devices could restrict energy use, so processes only use what they need. Alternatively, they could suggest new workflows or procedures that would be less wasteful. While many of these steps may seem small, they add up to substantial savings.

It’s Time to Embrace the IoT and Machine Learning

Without the IoT and machine learning, businesses can’t reach their full potential. These technologies enable savings companies couldn’t achieve otherwise. As they advance, they’ll only become more effective.

The Internet of Things and machine learning are reshaping the business world. Those that don’t take advantage of them now could soon fall behind.

The post 5 Ways the IoT and Machine Learning Improve Operations appeared first on Artificial Intelligence.

The research report on Big Data in Flight Operations Industry market gives thorough insights regarding various key trends that shape the industry expansion with regards to regional perspective and competitive spectrum. Furthermore, the document mentions the challenges and potential restrains along with latent opportunities which may positively impact the market outlook in existing and untapped business spaces. Moreover, it presents the case studies, including the ones related to COVID-19 pandemic, to convey better understanding of the industry to all the interested parties.

Key highlights from COVID-19 impact analysis:

• COVID-19 outlook worldwide and economic overview.
• Major shifts in the demand share and supply chain of the market.
• Immediate & long-term impact of COVID-19 on avails.

A gist of the regional landscape:

• The geographical spectrum of the Big Data in Flight Operations Industry market is segmented into as North America, Europe, Asia-Pacific, South America, Middle East & Africa, South East Asia.
• Insights about performance of each regional segment based on growth rate during the forecast period is mentioned in the report.
• Analysis of the revenue generated, sales garnered, and growth rate is presented in full detail.

Exclusive pointers from the Big Data in Flight Operations Industry market report:

• Competitive terrain of Big Data in Flight Operations Industry market is formulated with major companies like Digital flight operations,Hardware andOthers.
• Important information regarding the manufactured products, company profiles, industry remuneration, and production patterns is given in the report.
• The document contains details regarding the market share of major players along with their gross margins and price patterns.
• The product spectrum is defined by segments like Making better use of airspace,Improving safety,Reducing environmental impact andSaving fuel.
• Critical details pertaining to revenue and volume predictions of each product type is delivered in the report.
• Various other aspects involving market share, growth rate, and production patterns of every product segment during the analysis period are elaborated in the report.
• Application landscape of Big Data in Flight Operations Industry market is fragmented into Hainan Airlines,Singapore Airlines,Cathay Pacific Airways Limited,AirAsia,Emirates,Eva Air,Ana All Nipon Airways,Thai Airways,China Southern,Qatar Airways,Qantas Airways andThe Airline of Indonesia
• The document examines every application with precision and predicts their y-o-y growth rate during study duration.
• Detailed information about the competition trends and comprehensive analytical outlook of the supply chain in industry is defined.
• The report encloses Porter’s five forces analysis and SWOT analysis to understand feasibility of new project.

Reasons for Buying this Report:

• This report provides pin-point analysis for the evolving competitive dynamics
• It provides a forward-looking perspective on different factors driving or hindering the market growth.
• It provides a technological growth map over time to understand the market growth rate.
• It provides a five- to seven-year forecast evaluated based on how the market is predicted to grow.
• It helps in understanding the key product segments and their future Outlook.

To conclude, Big Data in Flight Operations Industry Industry report mentions the key geographies, market landscapes alongside the product price, revenue, volume, production, supply, demand, market growth rate, and forecast, etc. This report also provides SWOT analysis, investment feasibility analysis, and investment return analysis.

MAJOR TOC OF THE REPORT:

Chapter 1 Industry Overview

Chapter 2 Production Market Analysis

Chapter 3 Sales Market Analysis

Chapter 4 Consumption Market Analysis

Chapter 5 Production, Sales and Consumption Market Comparison Analysis

Chapter 6 Major Manufacturers Production and Sales Market Comparison Analysis

Chapter 7 Major Product Analysis

Chapter 8 Major Application Analysis

Chapter 9 Industry Chain Analysis

Chapter 10 Global and Regional Market Forecast

Chapter 11 Major Manufacturers Analysis

Chapter 12 New Project Investment Feasibility Analysis

Chapter 13 Conclusions

Chapter 14 Appendix

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Several factors have converged to force companies to make vital decisions about the future of their operations departments.

The most pressing issue is the ongoing Covid-19 pandemic. Government-enforced shelter-at-home orders worldwide have compelled financial institutions to set up their employees for success in remote environments. The situation no doubt will have long-lasting effects on the industry.

But before the pandemic reached a crescendo in the US, financial institutions faced several key issues with their operations teams.

The emergence of zero-fee equity trading caused teams handling treasury operations to become more cash management-oriented due to the need to be more value-driven than ever. At the same time, aging technology stacks forced companies into deciding whether to invest in current systems, or undertake a complete overhaul. Financial institutions were also faced with outsourcing capabilities outside of their core operations.

These issues today still hold true, and will continue to exist even in a post-Covid world.

As financial institutions build their operations with an eye toward the future, it spurs conversations about how best to handle these challenges, and technology will be at the forefront of this change. Emerging technology will transform how operations departments work. For example, DocuSign has begun to replace “wet signatures” on documents; videoconferencing services like Zoom have made remote work environments more feasible; and blockchain might have the ability to eliminate many reconciliation and P&S teams. Additionally, technologies such as artificial intelligence (AI) and machine learning eliminate and streamline reconciliations, doing away with entire departments.

It’s imperative for financial institutions to take advantage of the changes while future-proofing their teams. They can do this by moving to a cloud-based platform. First of all, the cloud allows for a more distributed workforce, which in this environment has become vitally important. Also, cloud allows firms to embrace microservices, as opposed to being locked into full front-to-back solutions. And perhaps most importantly, cloud allows users to be nimble when it comes to picking the tools they want to deploy, thus allowing them to focus their internal teams on building true value-add systems and solutions.

Future Proofing Ops

Changes in technology, particularly as they relate to data, are impacting today’s operations teams. For example, data is essential to the way companies will set up AI and machine learning going forward. In the past, companies might have gotten away with having an offshore ops team reviewing reference security data and enriching it post-trade. With AI and machine learning, that process can start up front so companies can focus on extracting real-time trade data.

And it’s the distributed nature of this data that is causing changes to how those employees work.

Data now is filtering in from multiple sources as opposed to a centralized location companies dealt with a decade ago. This development has been the proverbial “game-changer” as financial institutions seek how best to organize such vital information. At the same time, firms now view their own data as a commodity, which prompts a couple of questions: Should it be monetized? If yes, then how?

That said, the technology to ingest and leverage datasets has dramatically changed as well in the past three years, which has changed the role of operations teams.

Teams must now identify, fix, and prevent problems, and have a deep understanding of the technology being used. The path to future-proof operations teams will rely on hiring personnel familiar with the latest technology along with traditional skills such as market awareness. No longer will the prerequisite for jobs be simply knowing how databases are built and how to read them.

Financial institutions also will need to answer questions about how best to deal with technology shortfalls, either internally or via outsourcing.

Those firms should seek to outsource capabilities that are not unique to a company. More and more, financial institutions are moving toward a model of “keep your bread and butter internal, and outsource functions that don’t add differentiating value.”

While outsourcing has become more bespoke and modular than in the past, a company’s strategy will vary according to its size. For example, a large financial firm might want to leverage a third-party user interface to take advantage of industry scale and various resources, so there may no longer be a need to build internally. 

The end goal for these technology changes is to help ensure efficiency within the operations team, which extends to the entire organization. That will help organizations improve their response to customer inquiries and issues.

Come Together

Since every company is unique in its processes, and approaches operational challenges in its own way, the need for communication and collaboration with industry peers is important. In order to communicate harmoniously as an industry, we need to agree that the future of operations cannot rely on a system that only works for one bank.

Organizations like ISITC, Sifma, Isda, and the EDM Council exist to assist companies in accomplishing their end goals when it comes to modernizing the operations team with uniformity across financial institutions’ systems. Our organizations can help lead the way as part of the ecosystem by establishing standards that make sense for all companies.

Companies today need to have conversations about vital topics with the goal of identifying common understandings of various processes. The industry needs to collaborate and align on such aspects of the business. If not, we will repeat the mistakes of the past without transforming the operations teams of the future.

Lisa Iagatta is chair of ISITC, where she is responsible for the strategic direction and growth of the independent non-profit financial services organization. She is also director of account management at Tegra118. 

The post Future of Operations Hinges on Tech Approach, Industry Collaboration appeared first on Artificial Intelligence.

Business intelligence (BI) is nothing new to enterprises that have been relying on data processing and analysis to deliver insightful reports that reflect business performance.

These tools are a great match for enterprises that value the data their operations generate.

BI software and programs work together to turn data into actionable insights that can drive better business decisions and market strategies and, ultimately, drive revenue as a result.

Combined with the masses of external data amassing every second – whether that’s customers’ feedback and experience, competitor intelligence, seasonal buying habits, or otherwise – businesses can have a huge amount of data at their disposal.

While BI systems draw specific data from pre-defined sources to turn them into insights, big data technologies capture data from a variety of sources in real-time, regardless of their formats or structure.

Unlike BI, big data doesn’t answer critical questions that enterprises have, but rather, it provides them with new information that can prompt new questions that enterprises haven’t thought of asking.

Once enterprises have a better idea of what they want to find out, they can turn to BI tools to deliver the insights, and even make predictions.

Business impact

Since big data came into the picture, BI becomes just that much more conducive.

Through big data, BI can now deliver insights that enable businesses to better understand their customers, improve marketing techniques, make personalization possible and identify issues and opportunities that emerge in real-time.

Marketing strategies no longer have to a shot in the dark. With data varying from customer feedback to their journey records, enterprises can market products with a clear understanding of what customers want and need.

Enterprises can then leverage data alongside real-time market analysis data to develop and deliver new and improved products and services.

Big data technologies can help businesses augment their operations. Data doesn’t have to be externally sourced, it can come from internal systems and programs as well.

Siloes between teams and hiccups in workflows can be detected in real-time, allowing enterprises to redesign their processes effectively.

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