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Transitioning from a Software Engineering Role into a Management Role – InfoQ.com

Software engineers who want to become good at leading engineers can use everyday opportunities to practice management. Peter Gillard-Moss gave a talk at QCon London where he shared his experience with becoming a manager, and provided tips and ideas for engineers aiming to become a manager.

Gillard-Moss mentioned that he wanted to enter management because he believed his technical experience gave him the insight to lead and make decisions on behalf of the team. This was a belief shared by the people who put him in the manager role, he explained:

Whilst I was keen to lead, I was reluctant to manage. In the end, the actual responsibility of management meant that I struggled in those roles.

Looking back, the reason for this was mainly bad managers, Gillard-Moss said. He did also have good managers, but those managers were very non-conventional and positioned themselves more as leaders than managers. They were very hands off and focused more on giving me direction and leaving him to figure out the day-to-day responsibilities, as Gillard-Moss explained:

I was free to make technical decisions, but this didnt help me develop as a manager. The result was that I was struggling with non-technical aspects of working with people and I lacked confidence in my role and retreated into being a senior technical individual contributor.

Gillard-Moss suggested that engineers who want to become good at leading engineers should practice in the small. There are everyday opportunities for engineers to practise management, he said. You dont need authority. In fact many engineers who end up as engineering managers are often spotted because they are showing flares of management in their teams, as he explained:

It really could be as simple as picking up an epic and taking responsibility for it end-to-end. Organising the team to deliver it successfully, providing clear communication and working to remove obstacles so the rest of the team can stay focused. Or it could be running a team ritual and working to make it valuable and productive.

If you have a good manager then youll probably realise they are already giving you these sorts of opportunities and enabling you to do it, Gillard-Moss said.

To act as role models, engineering leaders have to live up to the standard they want to set. Your every move is being watched by your team, Gillard-Moss said. The behaviour you expect from the engineers you must show first:

If you say quality is important but every time a hard trade-off needs to be made you sacrifice quality. Or you say you want to enable independence but get involved in every decision. Then you arent role modelling.

As an engineering leader, I dont need to know all the technical decisions or be an expert in every framework we are using or have intricate knowledge of how the code is organised to get the team to an answer, Gillard-Moss said. But the team does, he explained:

The value my experience as an engineer brings is that I know when someone elses idea shows promise and I should get behind it. Or when another engineer disagrees I understand where they are coming from. And when the team shows me what it looks like, whether in code or in a diagram, I connect with what they think and feel about it too.

This doesnt mean you can be ignorant, Gillard-Moss said. You have to learn from your teams and listen to them, as they will naturally keep up with things. As an engineering leader, you can nurture and encourage that. Combine that with going to the gemba and observing teams doing real work and you will pick things up by osmosis, Gillard-Moss concluded.

InfoQ interviewed Peter Gillard-Moss about managing and leading engineering teams.

InfoQ: What challenges did you face when you became a manager?

Peter Gillard-Moss: I had a strong aversion to the word manager and the idea of management. A lot of this was down to ignorance. I didnt know what good management looked like or what it meant. And the ideas I did have of it were mainly negative. Authority, approval, inspection, delegation, and giving tough feedback. I also associated a lot of those skills with project management and I knew I didnt have those things "in my blood".

It took me a while before I learned what good management is and why it is important. And why leadership and management are two sides of a coin, not a dichotomy.

InfoQ: How can engineering leaders act as stakeholders for engineering?

Gillard-Moss: You have to be a stakeholder for engineering and engineers by representing. This isnt the same as "speaking on behalf".

Your role is to bring the stakeholders perspective and ensure their needs and concerns are part of the decision-making. When making tough decisions you need to bring the engineering perspective, and help people from other disciplines understand the trade-offs being made. Negotiate with other stakeholders so we can make the best decision for the organisation and its customers and employees.

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Map of International Women in Engineering day events #EnhancedByEngineering @INWED1919 #INWED24 – Adafruit Blog

Inwed.org.uk has shared a searchable map with International Women in Engineering Day events from around the world. Search by your home country or see which events are going on around the world here!

Explore INWED24 Events

Events from around the world are being held to celebrate INWED. Browse the map below to find one near you, or get inspired to create your own!

Adafruit publishes a wide range of writing and video content, including interviews and reporting on the maker market and the wider technology world. Our standards page is intended as a guide to best practices that Adafruit uses, as well as an outline of the ethical standards Adafruit aspires to. While Adafruit is not an independent journalistic institution, Adafruit strives to be a fair, informative, and positive voice within the community check it out here: adafruit.com/editorialstandards

Adafruit is on Mastodon, join in! adafruit.com/mastodon

Stop breadboarding and soldering start making immediately! Adafruits Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.

Have an amazing project to share? The Electronics Show and Tell is every Wednesday at 7pm ET! To join, head over to YouTube and check out the shows live chat well post the link there.

Join us every Wednesday night at 8pm ET for Ask an Engineer!

Join over 36,000+ makers on Adafruits Discord channels and be part of the community! http://adafru.it/discord

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UK sector groups announce partnership to empower women in engineering and manufacturing – The Manufacturer

To coincide with International Women in Engineering Day [23rd June 2024] organisations from across the STEM industries have announced their intention to work together to empower women in UK engineering and manufacturing.

This pledge to increase collaboration is the result of a roundtable hosted by Ann Watson, CEO of Enginuity, the charity dedicated to helping employers in the engineering and manufacturing sector find new ways to close skills gaps, which was attended by senior representatives from organisations such as Construction Inclusion Coalition,Energy & Utility Skills,Womens Engineering Society,Women in Sustainability Network, andWomens Utilities Network.

Chaired by Karen Boswell OBE, Managing Director of Baxi UK and Ireland, who in her career as a senior leader running engineering and manufacturing businesses has passionately advocated for diversity and inclusion, theparticipants discussed challenges facing women in the engineering and manufacturing sector, including the need for increased gender diversity and the issue of mid-career leavers.According to a recent study by Engineering UK, the proportion of women in engineering and technology roles has decreased from 16.5% to 15.7% in the past year, with a significant number of women leaving roles between the ages of 35 to 44.

Reflecting on this worrying trend, the participants discussed other challenges facing women in the engineering and manufacturing sector, underscoring the necessity for enhanced collaboration and the need to unite industries within the engineering and manufacturing sector. While acknowledging the unique needs of the different industries, the participants agreed to work together given the pressing need for a more unified and inclusive representation.

The result is theagreement, announced last Friday, to collaborate on the following key themes:

Communication Campaign for Attraction:

The group aims to generate a wealth of stories from women in the sector, enriching future campaigns and developing the central content repository. Channels such as TikTok and other social media platforms, will be key to attracting young women by showcasing engaging and inspiring stories from female engineers. The group is looking for women in engineering and manufacturing to share their career stories to inspire the next generation.Get involved.

Leadership and Culture for Retention:

The group will focus on highlighting the importance of leadership and organisational culture in retaining women in engineering roles. It aims to address the need for supportive and inclusive workplace environments that encourage women to thrive and progress in their careers. Sharing stories of successful female leaders and their experiences will promote a culture of mentorship and support within the industry.

System/Policy Changes Needed from the Government:

The group will advocate for policy changes at the governmental level to support gender diversity in engineering and manufacturing. With the General Election in early July, the new Government must support skills and education policies and systems that facilitate entry routes and career progression for women in the sector. Collaboration with policymakers will be essential to drive systemic change and ensure long-term improvements in gender diversity.

Ann Watson, CEO of Enginuity, comments:Encouraging more women to enter and stay in the UK engineering and manufacturing sector can play an important role in closing skills gaps. International Women in Engineering Day reminds us all that there is more to do to shift the dial and ensure that we have diverse talent joining and thriving in the sector.

Only by working in collaboration can we jointly take bigger steps towards greater inclusivity and representation in engineering and manufacturing. By bringing together diverse voices and experiences, the aim is to create a supportive and collaborative environment that empowers women at all stages of their careers. Through this unified effort, challenges can be addressed and will pave the way for a more equitable and dynamic future.

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Bright Engineering develops long-term growth opportunities in aerospace and defence – PES Media

Lancashire precision manufacturer Bright Engineering is currently preparing for a busy period as the 2024 Farnborough International Air show looms large on the industry calendar.

Bright will be again exhibiting on the Farnborough Aerospace Consortium (FAC) pavilion, a formula which has worked extremely well for the business on several previous occasions. Providing valuable access to the heart of the exhibition, and with its own dedicated area of the stand, participation with FAC allows Bright the opportunity to scope out new leads and opportunities, and critically a base for hosting meetings with existing customers who will be at the show.

The sales strategy for Bright Engineering in the last couple of years has been threefold: firstly latching onto opportunities in aerospace as the sector enjoys a sure and steady recovery. Secondly, the growth of defence opportunities has been significant Bright having seen a major upturn in the sector equating to roughly 10% of turnover. Finally, diversification across a number of manufacturing sectors remains a sure way of ensuring growth and stability.

Bright counts a number of advanced manufacturing sectors among its client base, including green energies, motorsport, semiconductor, science and laboratory. Sales director Steve Amey comments: "This deliberate diversification across our target industries, and particular efforts to back up our core turnover with new and emerging opportunities has been key to Bright's ongoing growth and stability. In fact we expect to increase sales turnover this year by a further 10% and grow our exports to the USA by around 20% compared to existing levels.

Over the last two years Bright Engineering has made multiple investments to strengthen its core offer and guarantee a robust quality offering for customers. This started with a major investment in the inspection department, doubling the floor space and modernising the facility.

With a view to making its inspection offering more data-driven and having information on tap for its Tier 1 and OEM customers, Bright invested in the latest measuring equipment, featuring Bluetooth integration and dovetailing capability with its Progress Plus ERP system.

Investment in equipment from companies like Keyence has been most appreciated by the client base, as it provides Bright with the opportunity to measure multiple parts at the same time, compile data quickly, efficiently and accurately, and assist customers with first article validation and any technical queries that arise following assembly of the product.

Steve Amey continues: "Investments in quality technology have a real and immediate impact on customers. Machining centres costing 300,000 are key to the operation but customers are comfortably familiar with them.

What customers really appreciate is a supplier willing to invest directly in quality, how we measure, trace, track and pack the product to ensure its conformity. We have had multiple large customers tell us that our inspection facilities are more impressive than their own. We are really proud of what we have created."

In fact Bright Engineering was able to demonstrate its capability so effectively that it was recently awarded BAE Systems approval, an achievement that is not regular or commonplace for most medium-sized subcontractors. As a fairly new, but highly capable supplier to BAE Systems Air Sector, Bright is well positioned for future growth and supporting new programmes.

Bright is proud of its ongoing commitment to apprenticeships and it counts a number of current trainees, and recently time-served engineers as contributors to BAE Systems projects. The business has also supported a number of young people through degree apprenticeships.

Director Jon Hoyle explains: "Operating our business in the Lancashire aerospace belt brings with it a number of opportunities, as well as competition, but it keeps us on our toes. It's easy as small business owners to say we can't compete and we can't offer decent opportunities, but we definitely can.

On a number of occasions now we've taken advantage of the Apprenticeship Levy system to provide high quality, funded degree programmes for our employees, and everybody wins. We get motivated, capable individuals who can support our growth and get involved in exciting aerospace prototype projects."

The company has also recently invested in 3D printing and intends to use it to streamline its fixture manufacture for both CNC machining and inspection. Bright believes this is yet another example of a modest investment having a sizeable positive impact on efficiency, as well as improving customer experience.

Bright Engineering http://www.brightengineering.co.uk

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Anisha Roberts: An apprentice’s inspirational journey into the world of engineering #EnhancedByEngineering … – Adafruit Blog

Learn about Anisha Roberts career journey this #INWED24. Via the Engineer:

Anishas inspiration came from a former colleague who, at the age of 60, decided to retrain in a field she had always been passionate about. This moment was as a catalyst for Anisha, prompting her to leave her teaching career and to explore apprenticeship opportunities in engineering. Reflecting on her decision, she recalls, If she [her colleague] can be that brave, then so can I. This mindset propelled her to embark on a new career path, one that would lead to personal growth and professional fulfilment. It shows that the impact of one woman pursuing their ambitions has an inspirational trickledown effect.

Learn more!

Adafruit publishes a wide range of writing and video content, including interviews and reporting on the maker market and the wider technology world. Our standards page is intended as a guide to best practices that Adafruit uses, as well as an outline of the ethical standards Adafruit aspires to. While Adafruit is not an independent journalistic institution, Adafruit strives to be a fair, informative, and positive voice within the community check it out here: adafruit.com/editorialstandards

Adafruit is on Mastodon, join in! adafruit.com/mastodon

Stop breadboarding and soldering start making immediately! Adafruits Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.

Have an amazing project to share? The Electronics Show and Tell is every Wednesday at 7pm ET! To join, head over to YouTube and check out the shows live chat well post the link there.

Join us every Wednesday night at 8pm ET for Ask an Engineer!

Join over 36,000+ makers on Adafruits Discord channels and be part of the community! http://adafru.it/discord

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Today is International Women in Engineering Day 2024 #EnhancedByEngineering @INWED1919 #INWED24 – Adafruit Blog

Every June 23 is International Women in Engineering Day. To share some inspiration and help get the word out we will be posting to the Adafruit blog all day!

The theme for 2024 is Enhanced by Engineering:

This year well be celebrating the amazing work that women engineers around the world are doing to support lives and livelihoods every day.

Were profiling the best, brightest and bravest women in engineering, who have #enhancedbyengineering peoples everyday lives and are helping to build towards a brighter future.

Be sure to check back throughout the day. For more resources here!

Adafruit publishes a wide range of writing and video content, including interviews and reporting on the maker market and the wider technology world. Our standards page is intended as a guide to best practices that Adafruit uses, as well as an outline of the ethical standards Adafruit aspires to. While Adafruit is not an independent journalistic institution, Adafruit strives to be a fair, informative, and positive voice within the community check it out here: adafruit.com/editorialstandards

Adafruit is on Mastodon, join in! adafruit.com/mastodon

Stop breadboarding and soldering start making immediately! Adafruits Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.

Have an amazing project to share? The Electronics Show and Tell is every Wednesday at 7pm ET! To join, head over to YouTube and check out the shows live chat well post the link there.

Join us every Wednesday night at 8pm ET for Ask an Engineer!

Join over 36,000+ makers on Adafruits Discord channels and be part of the community! http://adafru.it/discord

CircuitPython The easiest way to program microcontrollers CircuitPython.org

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Helping nonexperts build advanced generative AI models | MIT News | Massachusetts Institute of Technology – MIT News

The impact of artificial intelligence will never be equitable if theres only one company that builds and controls the models (not to mention the data that go into them). Unfortunately, todays AI models are made up of billions of parameters that must be trained and tuned to maximize performance for each use case, putting the most powerful AI models out of reach for most people and companies.

MosaicML started with a mission to make those models more accessible. The company, which counts Jonathan Frankle PhD 23 and MIT Associate Professor Michael Carbin as co-founders, developed a platform that let users train, improve, and monitor open-source models using their own data. The company also built its own open-source models using graphical processing units (GPUs) from Nvidia.

The approach made deep learning, a nascent field when MosaicML first began, accessible to far more organizations as excitement around generative AI and large language models (LLMs) exploded following the release of Chat GPT-3.5. It also made MosaicML a powerful complementary tool for data management companies that were also committed to helping organizations make use of their data without giving it to AI companies.

Last year, that reasoning led to the acquisition of MosaicML by Databricks, a global data storage, analytics, and AI company that works with some of the largest organizations in the world. Since the acquisition, the combined companies have released one of the highest performing open-source, general-purpose LLMs yet built. Known as DBRX, this model has set new benchmarks in tasks like reading comprehension, general knowledge questions, and logic puzzles.

Since then, DBRX has gained a reputation for being one of the fastest open-source LLMs available and has proven especially useful at large enterprises.

More than the model, though, Frankle says DBRX is significant because it was built using Databricks tools, meaning any of the companys customers can achieve similar performance with their own models, which will accelerate the impact of generative AI.

Honestly, its just exciting to see the community doing cool things with it, Frankle says. For me as a scientist, thats the best part. Its not the model, its all the amazing stuff the community is doing on top of it. That's where the magic happens.

Making algorithms efficient

Frankle earned bachelors and masters degrees in computer science at Princeton University before coming to MIT to pursue his PhD in 2016. Early on at MIT, he wasn't sure what area of computing he wanted to study. His eventual choice would change the course of his life.

Frankle ultimately decided to focus on a form of artificial intelligence known as deep learning. At the time, deep learning and artificial intelligence did not inspire the same broad excitement as they do today. Deep learning was a decades-old area of study that had yet to bear much fruit.

I dont think anyone at the time anticipated deep learning was going to blow up in the way that it did, Frankle says. People in the know thought it was a really neat area and there were a lot of unsolved problems, but phrases like large language model (LLM) and generative AI werent really used at that time. It was early days.

Things began to get interesting with the 2017 release of a now-infamous paper by Google researchers, in which they showed a new deep-learning architecture known as the transformer was surprisingly effective as language translation and held promise across a number of other applications, including content generation.

In 2020, eventual Mosaic co-founder and tech executive Naveen Rao emailed Frankle and Carbin out of the blue. Rao had read a paper the two had co-authored, in which the researchers showed a way to shrink deep-learning models without sacrificing performance. Rao pitched the pair on starting a company. They were joined by Hanlin Tang, who had worked with Rao on a previous AI startup that had been acquired by Intel.

The founders started by reading up on different techniques used to speed up the training of AI models, eventually combining several of them to show they could train a model to perform image classification four times faster than what had been achieved before.

The trick was that there was no trick, Frankle says. I think we had to make 17 different changes to how we trained the model in order to figure that out. It was just a little bit here and a little bit there, but it turns out that was enough to get incredible speed-ups. Thats really been the story of Mosaic.

The team showed their techniques could make models more efficient, and they released an open-source large language model in 2023 along with an open-source library of their methods. They also developed visualization tools to let developers map out different experimental options for training and running models.

MITs E14 Fund invested in Mosaics Series A funding round, and Frankle says E14s team offered helpful guidance early on. Mosaics progress enabled a new class of companies to train their own generative AI models.

There was a democratization and an open-source angle to Mosaics mission, Frankle says. Thats something that has always been very close to my heart. Ever since I was a PhD student and had no GPUs because I wasnt in a machine learning lab and all my friends had GPUs. I still feel that way. Why cant we all participate? Why cant we all get to do this stuff and get to do science?

Open sourcing innovation

Databricks had also been working to give its customers access to AI models. The company finalized its acquisition of MosaicML in 2023 for a reported $1.3 billion.

At Databricks, we saw a founding team of academics just like us, Frankle says. We also saw a team of scientists who understand technology. Databricks has the data, we have the machine learning. You can't do one without the other, and vice versa. It just ended up being a really good match.

In March, Databricks released DBRX, which gave the open-source community and enterprises building their own LLMs capabilities that were previously limited to closed models.

The thing that DBRX showed is you can build the best open-source LLM in the world with Databricks, Frankle says. If youre an enterprise, the skys the limit today.

Frankle says Databricks team has been encouraged by using DBRX internally across a wide variety of tasks.

Its already great, and with a little fine-tuning its better than the closed models, he says. Youre not going be better than GPT for everything. Thats not how this works. But nobody wants to solve every problem. Everybody wants to solve one problem. And we can customize this model to make it really great for specific scenarios.

As Databricks continues pushing the frontiers of AI, and as competitors continue to invest huge sums into AI more broadly, Frankle hopes the industry comes to see open source as the best path forward.

Im a believer in science and Im a believer in progress and Im excited that were doing such exciting science as a field right now, Frankle says. Im also a believer in openness, and I hope that everybody else embraces openness the way we have. That's how we got here, through good science and good sharing.

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Harness AIs potential and navigate disruption with Digital Realty – CIO

It may seem like artificial intelligence (AI) became a media buzzword overnight, but this disruptive technology has been at the forefront of our agenda for several years at Digital Realty. Weve seen how the advent of cloud technology significantly transformed the landscape of digital business, and AI is set to disrupt industries in ways we are only beginning to understand. The key, as always, is to be on the right side of disruption, by embracing change and leveraging it to your advantage.

Getting AI right is a raceenterprises are feeling the pressure to harness AI to build unique value ahead of their competitors.

Digital Realty anticipated how AI would disrupt IT infrastructure and began planning a roadmap to support our customers over six years ago, working with groundbreaking early adopters and learning along the way. Heres what weve learned is necessary to successfully navigate the inevitable disruption and come out ahead by harnessing AIs potential.

AIs evolution: Machine learning, deep learning, GenAI

AI encompasses a suite of rapidly evolving technologies. Its a journey that started in earnest during the early 2000s with machine learning (ML). ML crunches vast amounts of data to learn from results, discover patterns, make predictions, and even automate some tasks.

Then came deep learning in the 2010s, further enhancing perception capabilities in computer vision. This enabled object classification and detection, voice recognition, and even partly autonomous vehicles.

Now, we are witnessing the rise of generative AI in the 2020s, which emphasizes language mastery. Its implications are profound, given how language permeates every facet of an organizations activities institutional knowledge, communication, and processes.

The potential benefits are enormous:Accentureestimates that 40% of all working hours can be augmented by large language models like GPT-4 and 65% of language tasks can be transformed into more productive activities through augmentation and automation.

Crucially, all these AI technologies hinge on data. Thats why our focus at Digital Realty has always been about data, and managingData Gravity challenges, to help ensure our customers can efficiently store, analyze, and extract value from their data by providing the meeting place where companies, technologies, and data come together.

Cloud as a case study: What we learned

The cloud journey is a good case study for thinking about disruption. I remember its inception and the initial debates about whether the cloud was friend or foe, and many enterprises are still navigating through its profound impact on digital transformation.

Your data oceans feed your cloud workloads and applications, which then creates even more data. The big question now is how do you optimize this relationship to create maximum value?

Initially, cloud was accessed over the public internet, often with little thought to proximity andsecurity. Many enterprises are understanding that in practice, proximity, and security matter immensely and businesses can lose their competitive edge if they dont optimize each. In fact, Ive built my career on pioneering private cloud consumption and enabling businesses to optimize their digital transformations.

Digital Realty has been instrumental in transforming the cloud into a safe and efficient environment where businesses can drive unique value. Today, we manage over 2.4 GW (gigawatts) of power and enable connected campuses across the globe.Were working to lower barriers to optimizehybrid multi-cloudwithServiceFabric Connect, a private, software-definedinterconnectionsolution.

Having assisted many of our 5,000 customers in their cloud journey, were poised to do the same for your AI journey.

Unlock the value from your data with AI

Falling behind in AI could mean getting disrupted. Its a land rush to build unique value over competitors and to fend off new entrants like digital disruptors that arent contending with legacy infrastructure.

At Digital Realty, weve been tracking the evolution of AI since before ourInvestor Day in 2017, where we identified AI as a primary driver of next-generationdata centerrequirements. Digital Realty has been aligning our offerings to meet these emerging demands. We understood that our customers would need an AI-readyglobal data center platformpurpose-built to deploy and scale innovation and drive business value.

Digital Realty

Source: Digital Realty Investor Day presentation, Slide 18, 2017

Why does AI require an AI-ready data center platform?

AI, especially analytics, requires a specialized environment due to specific hardware and data processing requirements. Power density requirements for AI can be 5 to 10 times more than traditional data center functions, and the need for liquid cooling is fast approaching.

Digital Realtys solution? A range of state-of-the-art tools to build optimized AI architectures and the ability to digitally engineer deployments in virtual environments. Digital Realtys data center designs contain modularity and large capacity blocks to support legacy and high-density AI deployments, allinterconnectedwithServiceFabric, a global, purpose-built network fabric.

Were also committed to sustainable growth. We can support your sustainable data needs of today and tomorrow with 400 MW of space expected to come online in the next 18 months, 1 GW of renewable energy under contract, and our entire European portfolio and our UScolocationportfolio are 100% renewable powered.

Digital Realty has supported the cloud providers globally for years and we developed core competencies along the way that enable us to do the same for our customers who need a home for AI.

Stay innovative,reach out to us, and lets deploy AI in a way that transforms your organization.

As of March 31, 2023, and represents consolidated portfolio plus our managed portfolio of unconsolidated joint ventures based on our ownership percentage.

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Coordinate-based neural representations for computational adaptive optics in widefield microscopy – Nature.com

Ji, N. Adaptive optical fluorescence microscopy. Nat. Methods 14, 374380 (2017).

Article Google Scholar

Hampson, K. M. et al. Adaptive optics for high-resolution imaging. Nat. Rev. Methods Primer 1, 68 (2021).

Article Google Scholar

Zhang, Q. et al. Adaptive optics for optical microscopy [invited]. Biomed. Opt. Express 14, 1732 (2023).

Article Google Scholar

Rueckel, M., Mack-Bucher, J. A. & Denk, W. Adaptive wavefront correction in two-photon microscopy using coherence-gated wavefront sensing. Proc. Natl Acad. Sci. USA 103, 1713717142 (2006).

Article Google Scholar

Cha, J. W., Ballesta, J. & So, P. T. C. Shack-Hartmann wavefront-sensor-based adaptive optics system for multiphoton microscopy. J. Biomed. Opt. 15, 046022 (2010).

Article Google Scholar

Aviles-Espinosa, R. et al. Measurement and correction of in vivo sample aberrations employing a nonlinear guide-star in two-photon excited fluorescence microscopy. Biomed. Opt. Express 2, 3135 (2011).

Article Google Scholar

Azucena, O. et al. Adaptive optics wide-field microscopy using direct wavefront sensing. Opt. Lett. 36, 825827 (2011).

Article Google Scholar

Wang, K. et al. Rapid adaptive optical recovery of optimal resolution over large volumes. Nat. Methods 11, 625628 (2014).

Article Google Scholar

Wang, K. et al. Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue. Nat. Commun. 6, 7276 (2015).

Article Google Scholar

Paine, S. W. & Fienup, J. R. Machine learning for improved image-based wavefront sensing. Opt. Lett. 43, 1235 (2018).

Article Google Scholar

Asensio Ramos, A., De La Cruz Rodrguez, J. & Pastor Yabar, A. Real-time, multiframe, blind deconvolution of solar images. Astron. Astrophys. 620, A73 (2018).

Article Google Scholar

Nishizaki, Y. et al. Deep learning wavefront sensing. Opt. Express 27, 240 (2019).

Article Google Scholar

Andersen, T., Owner-Petersen, M. & Enmark, A. Neural networks for image-based wavefront sensing for astronomy. Opt. Lett. 44, 4618 (2019).

Article Google Scholar

Saha, D. et al. Practical sensorless aberration estimation for 3D microscopy with deep learning. Opt. Express 28, 29044 (2020).

Article Google Scholar

Wu, Y., Guo, Y., Bao, H. & Rao, C. Sub-millisecond phase retrieval for phase-diversity wavefront sensor. Sensors 20, 4877 (2020).

Article Google Scholar

Allan, G., Kang, I., Douglas, E. S., Barbastathis, G. & Cahoy, K. Deep residual learning for low-order wavefront sensing in high-contrast imaging systems. Opt. Express 28, 26267 (2020).

Article Google Scholar

Yanny, K., Monakhova, K., Shuai, R. W. & Waller, L. Deep learning for fast spatially varying deconvolution. Optica 9, 96 (2022).

Article Google Scholar

Hu, Q. et al. Universal adaptive optics for microscopy through embedded neural network control. Light: Sci. Appl. 12, 270 (2023)

Lehtinen, J. et al. Noise2Noise: learning image restoration without clean data. In Proc. 35th International Conference on Machine Learning Vol. 80 (eds Dy, J. & Krause, A.) 29652974 (PMLR, 2018).

Krull, A., Buchholz, T.-O. & Jug, F. Noise2Void - learning denoising from single noisy images. In Proc. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 21242132 (IEEE, 2019); https://doi.org/10.1109/CVPR.2019.00223

Platisa, J. et al. High-speed low-light in vivo two-photon voltage imaging of large neuronal populations. Nat. Methods 20, 10951103 (2023).

Li, X. et al. Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01450-8 (2022).

Article Google Scholar

Eom, M. et al. Statistically unbiased prediction enables accurate denoising of voltage imaging data. Nat. Methods 20, 15811592 (2022).

Ren, D., Zhang, K., Wang, Q., Hu, Q. & Zuo, W. Neural blind deconvolution using deep priors. In Proc. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 33383347 (IEEE, 2020); https://doi.org/10.1109/CVPR42600.2020.00340

Wang, F. et al. Phase imaging with an untrained neural network. Light: Sci. Appl. 9, 77 (2020).

Article Google Scholar

Bostan, E., Heckel, R., Chen, M., Kellman, M. & Waller, L. Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network. Optica 7, 559 (2020).

Article Google Scholar

Kang, I. et al. Simultaneous spectral recovery and CMOS micro-LED holography with an untrained deep neural network. Optica 9, 1149 (2022).

Article Google Scholar

Zhou, K. C. & Horstmeyer, R. Diffraction tomography with a deep image prior. Opt. Express 28, 12872 (2020).

Article Google Scholar

Sun, Y., Liu, J., Xie, M., Wohlberg, B. & Kamilov, U. CoIL: coordinate-based internal learning for tomographic imaging. IEEE Trans. Comput. Imaging 7, 14001412 (2021).

Article Google Scholar

Liu, R., Sun, Y., Zhu, J., Tian, L. & Kamilov, U. Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields. Nat. Mach. Intell. 4, 781791 (2022).

Kang, I. et al. Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits. Optica 10, 10001008 (2023).

Article Google Scholar

Chan, T. F. & Chiu-Kwong, W. Total variation blind deconvolution. IEEE Trans. Image Process. 7, 370375 (1998).

Article Google Scholar

Levin, A., Weiss, Y., Durand, F. & Freeman, W. T. Understanding and evaluating blind deconvolution algorithms. In Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition 19641971 (IEEE, 2009); https://doi.org/10.1109/CVPR.2009.5206815

Perrone, D. & Favaro, P. Total variation blind deconvolution: the devil is in the details. In Proc. 2014 IEEE Conference on Computer Vision and Pattern Recognition 29092916 (IEEE, 2014); https://doi.org/10.1109/CVPR.2014.372

Jin, M., Roth, S. & Favaro, P. in Computer Vision ECCV 2018. ECCV 2018. Lecture Notes in Computer Science Vol. 11211 (eds Ferrari, V. et al.) 694711 (Springer, 2018).

Hornik, K., Stinchcombe, M. & White, H. Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359366 (1989).

Article Google Scholar

Cybenko, G. Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2, 303314 (1989).

Tewari, A. et al. Advances in neural rendering. In ACM SIGGRAPH 2021 Courses, 1320 (Association for Computing Machinery, 2021).

Tancik, M. et al. in Advances in Neural Information Processing Systems Vol. 33 (eds Larochelle, H. et al.) 75377547 (Curran Associates, 2020).

Mildenhall, B. et al. NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65, 99106 (2022).

Article Google Scholar

Perdigao, L., Shemilt, L. A. & Nord, N. rosalindfranklininstitute/RedLionfish v.0.9. Zenodo https://doi.org/10.5281/zenodo.7688291 (2023).

Richardson, W. H. Bayesian-based iterative method ofimage restoration*. J. Opt. Soc. Am. 62, 55 (1972).

Article Google Scholar

Lucy, L. B. An iterative technique for the rectification of observed distributions. Astron. J. 79, 745 (1974).

Article Google Scholar

Sitzmann, V. et al. Scene representation networks: continuous 3D-structure-aware neural scene representations. In Proc. 33rd International Conference on Neural Information Processing Systems Vol. 32 (eds Wallach, H. et al.) 11211132 (Curran Associates, 2019).

Martel, J. N. P. et al. ACORN: adaptive coordinate networks for neural scene representation. ACM Trans. Graph. 40, 113 (2021).

Zhao, H., Gallo, O., Frosio, I. & Kautz, J. Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3, 4757 (2017).

Article Google Scholar

Kang, I., Zhang, F. & Barbastathis, G. Phase extraction neural network (PhENN) with coherent modulation imaging (CMI) for phase retrieval at low photon counts. Opt. Express 28, 21578 (2020).

Article Google Scholar

Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://doi.org/10.48550/arXiv.1412.6980 (2017).

Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Proc. 33rd International Conference on Neural Information Processing Systems (eds Wallach, H. M. et al.) 721 (Curran Associates, 2019).

Turcotte, R., Liang, Y. & Ji, N. Adaptive optical versus spherical aberration corrections for in vivo brain imaging. Biomed. Opt. Express 8, 38913902 (2017).

Article Google Scholar

Kolouri, S., Park, S. R., Thorpe, M., Slepcev, D. & Rohde, G. K. Optimal mass transport: signal processing and machine-learning applications. IEEE Signal Process Mag. 34, 4359 (2017).

Article Google Scholar

Villani, C. Topics in Optimal Transportation Vol. 58 (American Mathematical Society, 2021).

Turcotte, R. et al. Dynamic super-resolution structured illumination imaging in the living brain. Proc. Natl Acad. Sci. USA 116, 95869591 (2019).

Article Google Scholar

Li, Z. et al. Fast widefield imaging of neuronal structure and function with optical sectioning in vivo. Sci. Adv. 6, eaaz3870 (2020).

Article Google Scholar

Zhang, Q., Pan, D. & Ji, N. High-resolution in vivo optical-sectioning widefield microendoscopy. Optica 7, 1287 (2020).

Article Google Scholar

Zhao, Z. et al. Two-photon synthetic aperture microscopy for minimally invasive fast 3D imaging of native subcellular behaviors in deep tissue. Cell 186, 24752491.e22 (2023).

Article Google Scholar

Wu, J. et al. Iterative tomography with digital adaptive optics permits hour-long intravital observation of 3D subcellular dynamics at millisecond scale. Cell 184, 33183332.e17 (2021).

Article Google Scholar

Gerchberg, R. W. A practical algorithm for the determination of plane from image and diffraction pictures. Optik 35, 237246 (1972).

Google Scholar

Flamary, R. et al. POT: Python optimal transport. J. Mach. Learn. Res. 22, 18 (2021).

Google Scholar

Holmes, T. J. et al. in Handbook of Biological Confocal Microscopy (ed. Pawley, J. B.) 389402 (Springer, 1995).

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Prediction of hepatic metastasis in esophageal cancer based on machine learning | Scientific Reports – Nature.com

Esophageal cancer is a remarkably fatal malignancy, with a prevalence of distant metastases reaching up to 42% in newly diagnosed patients, prominently affecting the liver as the most frequently involved organ26,27,28. The effective treatment and comprehensive management of metastatic esophageal cancer necessitate a multimodal strategy, which continues to pose significant challenges. Therefore, it is of crucial significance for clinical decision-making to identify high-risk factors of esophageal cancer and accurately predict whether patients will develop liver metastasis based on their individual and unique clinical and pathological characteristics.

Currently, the HM of advanced esophageal cancer remains understudied in the scientific literature. Prognostic research in this domain is predominantly focused on two key aspects. Firstly, there is a conspicuous paucity of exploratory investigations into the high-risk prognostic factors associated with esophageal cancer. Additionally, further exploration of the interrelationships among these independent prognostic factors is noticeably lacking. Secondly, there is a dearth of research on HM models for advanced esophageal cancer that leverage the immense potential of big data. Consequently, there is an urgent need for comprehensive studies in these areas to contribute to an improved understanding and accurate prognostication of advanced esophageal cancer.

Some studies believe that smoking and drinking are the most common risk factors for male esophageal cancer29. Some previous studies30 have also shown that for cancer patients, the degree of tissue differentiation, pathological N-stage, vascular invasion, and neuroinvasion are recognized factors that affect the prognosis of patients with esophageal cancer31,32,33,34. The conclusions of these studies lacked the support of big data and did not address the prediction on HM of advanced esophageal cancer. Based on big data analysis of SEER database, our study screened out independent high risk factors associated with HM by logistic regression analysis. This study included 15 clinically common relevant factors associated with advanced esophageal cancer with liver metastasis, which are: age, sex, Marital status, Race, Primary Site, Tumor histology, Tumor grade, T stage, N stage, Surgery, Radiation, Chemotherapy, Brain metastasis, Bone metastasis, Lung metastasis. To identify the independence between features, we obtained a correlation heat map by Spearman correlation analysis. There was no strong correlation among these 15 features by the Fig.2A. Moreover, 11 independent high risk factors related to liver metastasis were screened by logistic regression analysis, which were as follows: age, Primary Site, Tumor histology, Tumor grade, T stage, N stage, Surgery, Radiation, Chemotherapy, Bone metastasis, Lung metastasis.

Undoubtedly, the construction of prediction models for HM of advanced esophageal cancer is equally significant to the exploration of independent high risk factors in this context. Presently, there is a notable dearth of studies focused on risk factors in esophageal cancer patients with distant organ metastases35. For instance, Tang et al. previously constructed a nomogram to predict the survival of patients with metastatic esophageal cancer; however, this study encompassed metastases to all anatomical sites, without specifically exploring a prediction model for predicting the risk of distant metastasis36. Similarly, Cheng et al. established models for predicting both the risk and survival of esophageal cancer patients, albeit those specifically tailored to brain metastasis37. Furthermore, Guo et al. provided detailed characteristics and explored risk and prognostic factors for patients with liver metastasis, yet they did not develop any predictive tools38. Considering that liver metastasis represents the most common site of distant spread, conducting a comprehensive investigation specifically targeting esophageal cancer patients with liver metastasis assumes paramount clinical importance.

Previous studies have constructed nomograms to predict EC metastasis based on traditional logistic models. However, the limitations of this method in prediction accuracy and processing big data have made it difficult to make great breakthroughs in precision medicine9,10. And traditional research cannot exploration the interaction between different independent high risk factors18,19. In contrast, our study can better document complex associations between different independent high risk factors, thereby improving the accuracy of the model20. Previous studies have used nomogram methods to build a model for predicting the metastasis of patients with esophageal cancer based on the data of patients with esophageal cancer in the SEER database, but these studies did not involve the establishment of a predicting model for HM of advanced metastatic esophageal cancer by ML21.

We then constructed six prediction models using ML, Internal ten-fold cross-validation (Fig.3A) showed that GBM model performed best among the six models. Leveraging these findings, we have successfully devised an openly accessible online calculator (https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/) based on the GBM model. The model we have developed accurately predicts patients' risk of HM based on various clinical indicators. Clinicians can access this model through the provided website to input patient information and obtain corresponding predictions of hepatic metastases, thereby facilitating clinical decision-making.

Our research has the following advantages. Firstly, this study established a statistical model based on machine learning that can predict the HM of patients with EC. To the best of our knowledge, we are the first to use ML to construct a prediction model of LM of EC. This model is more reliable than the traditional nomogram prediction model. And this work expanded our knowledge of advanced EC. Second, our study further explores the relationship between different independent high risk factors, which provides a new direction for future clinical research. In other words, clinical research should not only explore the metastasis of patients, but also explore the correlation between different independent high risk factors, so as to better find the relationship between these factors and further eliminate the factors that are not conducive to the metastasis of patients during perioperative period.

Meanwhile, this study has some limitations. First, Current machine learning is almost entirely statistical or black-box, bring severe theoretical limitations to its performance23. Second, this study is a single-center study with limited number of patients included, and the application of machine learning model on large data sets can obtain more stable results22. Therefore, in subsequent studies, multi-center data can be added for training and external verification, so as to obtain a more reliable prediction model. Third, this study did not include neoadjuvant therapy, surgical methods, circulating tumor DNA and other factors that may affect the long-term prognosis of patients with esophageal cancer. In the future, with the continuous improvement of the database, we will incorporate more correlation parameters associated with the HM of EC into the web predictor to improve its adaptability.

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Prediction of hepatic metastasis in esophageal cancer based on machine learning | Scientific Reports - Nature.com

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