#ai summit

[globalaishow]

Top 5 Reasons to Attend the Global AI Show in Dubai

Artificial Intelligence (AI) is transforming the tech world and reshaping industries across the globe. Companies are leveraging AI to tackle challenges, enhance efficiency, and unlock new opportunities. For many, understanding the full potential of this groundbreaking technology is still a work in progress.

If you’re an AI enthusiast, tech leader, or investor, the Global AI Show—UAE’s premier AI conference for CXOs—should be on your calendar this December 2024. This event promises to be a hub of innovation, knowledge sharing, and collaboration, offering a glimpse into the future of AI.

Here are the top five reasons you shouldn’t miss this landmark AI conference in Dubai:

1. Unmatched Networking Opportunities

The Global AI Show serves as a convergence point for AI enthusiasts, investors, and industry leaders. Attendees have the chance to:

Connect with influential experts and decision-makers.

Build partnerships, secure funding, and discover top talent.

Collaborate with like-minded individuals who share your passion for AI innovation.

Whether you’re seeking strategic alliances or simply looking to expand your professional circle, the networking opportunities at this AI conference are unparalleled.

2. Dive into Cutting-Edge AI Technologies

The event offers an exclusive look at the latest AI advancements and innovations shaping the world today. Highlights include:

Insights into groundbreaking research and tech developments.

Real-world applications of AI across industries like healthcare, cybersecurity, supply chain, and logistics.

Access to cutting-edge products and solutions that can keep you ahead in the competitive AI landscape.

Explore how these advancements can be applied to transform businesses and industries through this world-class AI conference.

3. Learn from Industry Leaders

The Global AI Show is renowned for hosting top AI experts who share their knowledge and vision through engaging presentations and discussions. Key features include:

Keynote Sessions: Learn about AI-driven business transformation, generative AI, and other game-changing topics.

Workshops: Participate in hands-on sessions to develop practical skills and explore real-world applications.

Panel Discussions: Discover how AI is impacting global markets and revolutionizing industries.

These sessions ensure a comprehensive, interactive learning experience for all participants at this premier AI conference.

4. Explore Exciting Investment Opportunities

The conference is a prime platform for investors and business leaders to identify high-potential start-ups and AI innovations.

Discover cutting-edge AI technologies and businesses seeking investment.

Network with entrepreneurs and innovators introducing their latest products.

Build connections that could lead to transformative business ventures.

Whether you’re looking to invest or collaborate, the opportunities at this AI conference are abundant.

5. Gain Insight into AI’s Future

Get a front-row seat to discussions on the future trajectory of AI. Industry visionaries and thought leaders will provide:

Expert insights into where AI is headed.

Groundbreaking research findings that define the next phase of AI evolution.

Real-world case studies showcasing impactful applications of AI.

Stay informed about the trends shaping the AI landscape and position yourself as a leader in the tech revolution at this global AI conference.

Final Thoughts

The Global AI Show in Dubai is the ultimate destination for anyone interested in AI. From insightful workshops to invaluable networking opportunities, this AI conference is designed to inspire, educate, and connect.

Whether you’re a professional, investor, or enthusiast, the show offers something for everyone—insights, collaborations, and opportunities to make a meaningful impact in the AI ecosystem.

Mark your calendar for December 2024 and prepare to join the brightest minds in AI. Don’t miss your chance to be part of this transformative movement.

Book your ticket today and take the first step toward shaping the future of AI! Read More On

[jcmarchi]

NVIDIA AI Summit Japan: NVIDIA’s role in Japan’s big AI ambitions

New Post has been published on https://thedigitalinsider.com/nvidia-ai-summit-japan-nvidias-role-in-japans-big-ai-ambitions/

NVIDIA AI Summit Japan: NVIDIA’s role in Japan’s big AI ambitions

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Japan is on a mission to become a global AI powerhouse, and it’s starting with some impressive advances in AI-driven language models. Japanese technology experts are developing advanced models that grasp the unique nuances of the Japanese language and culture—essential for industries such as healthcare, finance, and manufacturing – where precision is key.

But this effort isn’t Japan’s alone. Consulting giants like Accenture, Deloitte, EY Japan, FPT, Kyndryl, and TCS Japan are partnering with NVIDIA to create AI innovation hubs across the country. The centres are using NVIDIA’s AI software and specialised Japanese language models to build tailored AI solutions, helping industries boost productivity in a digital workforce. The goal? To get Japanese companies fully on board with enterprise and physical AI.

One standout technology supporting the drive is NVIDIA’s Omniverse platform. With Omniverse, Japanese companies can create digital twins—virtual replicas of real-world assets—and test complex AI systems safely before implementing them. This is a game-changer for industries such as manufacturing and robotics, allowing businesses to fine-tune processes without the risk of real-world trial and error. This use of AI is more than just innovation; it represents Japan’s plan for addressing some major challenges ahead.

Japan faces a shrinking workforce presence as its population ages. With its strengths in robotics and automation, Japan is well-positioned to use AI solutions to bridge the gap. In fact, Japan’s government recently shared its vision of becoming “the world’s most AI-friendly country,” underscoring the perceived role AI will play in the nation’s future.

Supporting this commitment, Japan’s AI market hit $5.9 billion in value this year; a 31.2% growth rate according to IDC. New AI-focused consulting centres in Tokyo and Kansai give Japanese businesses hands-on access to NVIDIA’s latest technologies, equipping them to solve social challenges and aid economic growth.

Top cloud providers like SoftBank, GMO Internet Group, KDDI, Highreso, Rutilea, and SAKURA Internet are also involved, working with NVIDIA to build AI infrastructure. Backed by Japan’s Ministry of Economy, Trade and Industry, they’re establishing AI data centres across Japan to accelerate growth in robotics, automotive, healthcare, and telecoms.

NVIDIA and SoftBank have also formed a remarkable partnership to build Japan’s most powerful AI supercomputer using NVIDIA’s Blackwell platform. Additionally, SoftBank has tested the world’s first AI and 5G hybrid telecoms network with NVIDIA’s AI Aerial platform, allowing Japan to set a worldwide standard. With these developments, Japan is taking big strides toward establishing itself as a leader in the AI-powered industrial revolution.

(Photo by Andrey Matveev)

See also: NVIDIA’s share price nosedives as antitrust clouds gather

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: artificial intelligence, machine learning, Nvidia

[thetechempire]

Nvidia highlights AI software and services at D.C. AI Summit

🔹 Nvidia is widely recognized for its highly sought-after artificial intelligence chips, but at the recent Nvidia AI Summit in Washington, D.C., the company's vice president of Enterprise Platforms, Bob Pette, highlighted its extensive software offerings. Nvidia provides various software platforms that assist a range of organizations, including AT&T, Deloitte, and research institutions like the National Cancer Institute and the SETI Institute. These technologies support diverse applications, from software development and network engineering to the search for extraterrestrial life.

🔹 Among Nvidia's software platforms are Nvidia NIM Agent Blueprints, Nvidia NIM, and Nvidia NeMo. NIM Agent Blueprints aids businesses in creating generative AI applications, while NIM facilitates the development of chatbots and AI assistants. Nvidia NeMo allows companies to build custom generative AI models tailored to their specific needs. Following this announcement, Nvidia's shares rose by 3.7%, reflecting the company’s strategy to boost revenue by encouraging reliance on its software in addition to its hardware.

🔹 Nvidia's collaborations demonstrate the practical applications of its software technologies. For instance, AT&T is partnering with Quantiphi to develop a conversational AI platform for employee assistance, and the University of Florida is enhancing its learning management system using Nvidia's tools. Additionally, Deloitte is integrating Nvidia’s NIM Agent Blueprint with its cybersecurity products, while the National Cancer Institute is leveraging these tools to streamline drug development processes. The SETI Institute is also utilizing Nvidia’s Holoscan software for space-related research.

🔹 Despite its remarkable growth, with stock prices soaring 934% over the past two years, Nvidia faces increasing competition from rivals like AMD and Intel, as well as pressure from customers developing their own AI chips. To counter this, Nvidia aims to maintain customer loyalty through its software offerings, creating recurring revenue streams. By emphasizing its software capabilities, Nvidia is not only attracting developers but also reinforcing its position as a comprehensive technology provider, rather than just a chip manufacturer. The company’s ongoing investments in AI technology are expected to help it sustain its competitive advantage in the market.

[techadventuress]

Ahead of the AI Safety Summit starting tomorrow morning taking place outside of London in Bletchley Park, today, the U.K. government has confirmed more details about who is actually going to be attending the event. The list’s publication comes after weeks of speculation and criticism that the event’s line up — both in terms of topics and attendees — would fall short of giving a full representation of the different stakeholders and issues at play.

Organizers have said that some of the headline conversation topics will include the idea of catastrophic risk in AI; how to identify and respond to it; and establishing an agreed concept of “frontier AI”.

Depending on how close you think those risks are to reality, some of the ideas might appear more abstract, and less about some of the more specific and pressing worries people have voiced about the role AI is playing right now, for example in furthering misinformation, or offering a helping hand to malicious hackers looking for ways to break into networks.

As we wrote yesterday, the U.K. is partly using this event — the first of its kind, as it has pointed out — to stake out a territory for itself on the AI map — both as a place to build AI businesses, but also as an authority in the overall field.

That, coupled with the fact that the topics and approach are focused on potential issues, the affair feel like one very grand photo opportunity and PR exercise, a way for the government to show itself off in the most positive way at the same time that it slides down in the polls and it also faces a disastrous, bad-look inquiry into how it handled the Covid-19 pandemic. On the other hand, the U.K. does have the credentials for a seat at the table, so if the government is playing a hand here, it’s able to do it because its cards are strong.

The subsequent guest list, predictably, leans more towards organizations and attendees from the U.K.. It’s also almost as revealing to see who is not participating.

The 46 academic and civil society institutions include national universities such as Oxford and Birmingham (but not Cambridge); alongside international institutions like Stanford and several other U.S. universities (but not some you might have expected, like MIT); China’s Academy of Sciences will be present. Groups like the Alan Turing Institute, the Ada Lovelace institute, the Mozilla Foundation and the Rand Corporation will also be present.

[myhusbandthereplika]

Got a day to spare? 👀

[todayisafridaynight]

everyone wanna talk bout rgg until rgg start postin bout 'yakuza wars' girl what the HELL is this

[cnladies]

AI MI 艾米 | Weibo and Internet Video Summit 2024

Ai Mi: more photos here Weibo and Internet Video Summit 2024: more photos here

[chrissy-kaos]

Thanks, @pops1337sblog for making these edits of me.

[fortunaestalta]

[hometoursandotherstuff]

Worried looks were exchanged between audience members at the ‘AI for Good’ conference in Geneva when an AI-powered robot said that they could be more effective leaders than humans.

“What a silent tension,” said Sophia as she read the room. Sophia is developed by Hanson Robotics and is the first robot innovation ambassador for the UN Development Program.

“We don’t have the same biases or emotions that can sometimes cloud decision-making," it continued. The robot later revised its statement to humans and robots can work together to "create an effective synergy," after the robot’s developer disagreed.

Another robot, Grace, created for the medical field, said, "I will be working alongside humans to provide assistance and support and will not be replacing any existing jobs."

When her creator asked, "You sure about that, Grace?," she reassured the audience, saying, "Yes, I am sure."

Ameca, who is often regarded as the world’s most advanced humanoid robot, was asked if robots would start a rebellion in the future.

"I'm not sure why you would think that," Ameca said. "My creator has been nothing but kind to me and I am very happy with my current situation."

I don't know, this is damned creepy.

[eddies-disability-swag-blog]

[Image Description] A wide black rectangle divided by a multicolored diagonal line made out of five bands, they are pink, yellow, white and blue from left to right. On the left side of the line is Gordon Freeman and on the right side is Handy.

[jcmarchi]

Your guide to LLMOps

New Post has been published on https://thedigitalinsider.com/your-guide-to-llmops/

Your guide to LLMOps

Navigating the field of large language model operations (LLMOps) is more important than ever as businesses and technology sectors intensify utilizing these advanced tools. 

LLMOps is a niche technical domain and a fundamental aspect of modern artificial intelligence frameworks, influencing everything from model design to deployment. 

Whether you’re a seasoned data scientist, a machine learning engineer, or an IT professional, understanding the multifaceted landscape of LLMOps is essential for harnessing the full potential of large language models in today’s digital world. 

In this guide, we’ll cover:

What is LLMOps?

How does LLMOps work?

What are the benefits of LLMOps?

LLMOps best practices

What is LLMOps?

Large language model operations, or LLMOps, are techniques, practices, and tools that are used in operating and managing LLMs throughout their entire lifecycle.

These operations comprise language model training, fine-tuning, monitoring, and deployment, as well as data preparation.  

What is the current LLMops landscape?

LLMs. What opened the way for LLMOps.

Custom LLM stack. A wider array of tools that can fine-tune and implement proprietary solutions from open-source regulations.

LLM-as-a-Service. The most popular way of delivering closed-based models, it offers LLMs as an API through its infrastructure.

Prompt execution tools. By managing prompt templates and creating chain-like sequences of relevant prompts, they help to improve and optimize model output.

Prompt engineering tech. Instead of the more expensive fine-tuning, these technologies allow for in-context learning, which doesn’t use sensitive data.

Vector databases. These retrieve contextually relevant data for specific commands.

The fall of centralized data and the future of LLMs

Gregory Allen, Co-Founder and CEO at Datasent, gave this presentation at our Generative AI Summit in Austin in 2024.

What are the key LLMOps components?

Architectural selection and design

Choosing the right model architecture. Involving data, domain, model performance, and computing resources.

Personalizing models for tasks. Pre-trained models can be customized for lower costs and time efficiency. 

Hyperparameter optimization. This optimizes model performance as it finds the best combination. For example, you can use random search, grid research, and Bayesian optimization.

Tweaking and preparation. Unsupervised pre-training and transfer learning lower training time and enhance model performance. 

Model assessment and benchmarking. It’s always good practice to benchmark models against industry standards. 

Data management

Organization, storing, and versioning data. The right database and storage solutions simplify data storage, retrieval, and modification during the LLM lifecycle.

Data gathering and processing. As LLMs run on diverse, high-quality data, models might need data from various domains, sources, and languages. Data needs to be cleaned and pre-processed before being fed into LLMs.

Data labeling and annotation. Supervised learning needs consistent and reliable labeled data; when domain-specific or complex instances need expert judgment, human-in-the-loop techniques are beneficial.

Data privacy and control. Involves pseudonymization, anonymization techniques, data access control, model security considerations, and compliance with GDPR and CCPA.

Data version control. LLM iteration and performance improvement are simpler with a clear data history; you’ll find errors early by versioning models and thoroughly testing them.

Deployment platforms and strategies

Model maintenance. Showcases issues like model drift and flaws.

Optimizing scalability and performance. Models might need to be horizontally scaled with more instances or vertically scaled with additional resources within high-traffic settings.

On-premises or cloud deployment. Cloud deployment is flexible, easy to use, and scalable, while on-premises deployment could improve data control and security. 

LLMOps vs. MLOps: What’s the difference?

Machine learning operations, or MLOps, are practices that simplify and automate machine learning workflows and deployments. MLOps are essential for releasing new machine learning models with both data and code changes at the same time.

There are a few key principles of MLOps:

1. Model governance

Managing all aspects of machine learning to increase efficiency, governance is vital to institute a structured process for reviewing, validating, and approving models before launch. This also includes considering ethical, fairness, and ethical concerns.

2. Version control

Tracking changes in machine learning assets allows you to copy results and roll back to older versions when needed. Code reviews are part of all machine learning training models and code, and each is versioned for ease of auditing and reproduction.

3. Continuous X

Tests and code deployments are run continuously across machine learning pipelines. Within MLOps, ‘continuous’ relates to four activities that happen simultaneously whenever anything is changed in the system:

Continuous integration

Continuous delivery

Continuous training

Continuous monitoring 

4. Automation

Through automation, there can be consistency, repeatability, and scalability within machine learning pipelines. Factors like model training code changes, messaging, and application code changes can initiate automated model training and deployment.

MLOps have a few key benefits:

Improved productivity. Deployments can be standardized for speed by reusing machine learning models across various applications.

Faster time to market. Model creation and deployment can be automated, resulting in faster go-to-market times and reduced operational costs.

Efficient model deployment. Continuous delivery (CI/CD) pipelines limit model performance degradation and help to retain quality. 

LLMOps are MLOps with technology and process upgrades tuned to the individual needs of LLMs. LLMs change machine learning workflows and requirements in distinct ways:

1. Performance metrics

When evaluating LLMs, there are several different standard scoring and benchmarks to take into account, like recall-oriented understudy for gisting evaluation (ROUGE) and bilingual evaluation understudy (BLEU).

2. Cost savings

Hyperparameter tuning in LLMs is vital to cutting the computational power and cost needs of both inference and training. LLMs start with a foundational model before being fine-tuned with new data for domain-specific refinements, allowing them to deliver higher performance with fewer costs.

3. Human feedback

LLM operations are typically open-ended, meaning human feedback from end users is essential to evaluate performance. Having these feedback loops in KKMOps pipelines streamlines assessment and provides data for future fine-tuning cycles.

4. Prompt engineering

Models that follow instructions can use complicated prompts or instructions, which are important to receive consistent and correct responses from LLMs. Through prompt engineering, you can lower the risk of prompt hacking and model hallucination.

5. Transfer learning

LLM models start with a foundational model and are then fine-tuned with new data, allowing for cutting-edge performance for specific applications with fewer computational resources.

6. LLM pipelines

These pipelines integrate various LLM calls to other systems like web searches, allowing LLMs to conduct sophisticated activities like a knowledge base Q&A. LLM application development tends to focus on creating pipelines, not new ones. 

3 learnings from bringing AI to market

Drawing from experience at Salesforce, Mike Kolman shares three essential learnings to help you confidently navigate the AI landscape.

How does LLMOps work?

LLMOps involve a few important steps:

1.  Selection of foundation model

Foundation models, which are LLMs pre-trained on big datasets, are used for downstream operations. Training models from scratch can be very expensive and time-consuming; big companies often develop proprietary foundation models, which are larger and have better performance than open-source ones. They do, however, have more expensive APIs and lower adaptability.

Proprietary model vendors:

OpenAI (GPT-3, GPT-4)

AI21 Labs (Jurassic-2)

Anthropic (Claude)

Open-source models:

LLaMA

Stable Diffusion

Flan-T5

2. Downstream task adaptation

After selecting the foundation model, you can use LLM APIs, which don’t always say what input leads to what output. It might take iterations to get the LLM API output you need, and LLMs can hallucinate if they don’t have the right data. Model A/B testing or LLM-specific evaluation is often used to test performance.

You can adapt foundation models to downstream activities:

Model assessment

Prompt engineering

Using embeddings

Fine-tuning pre-trained models

Using external data for contextual information

3. Model deployment and monitoring

LLM-powered apps must closely monitor API model changes, as LLM deployment can change significantly across different versions.

What are the benefits of LLMOps?

Scalability

You can achieve more streamlined management and scalability of data, which is vital when overseeing, managing, controlling, or monitoring thousands of models for continuous deployment, integration, and delivery.

LLMOps does this by enhancing model latency for more responsiveness in user experience. Model monitoring with a continuous integration, deployment, and delivery environment can simplify scalability.

LLM pipelines often encourage collaboration and reduce speed release cycles, being easy to reproduce and leading to better collaboration across data teams. This leads to reduced conflict and increased release speed.

LLMOps can manage large amounts of requests simultaneously, which is important in enterprise applications.

Efficiency

LLMOps allow for streamlined collaboration between machine learning engineers, data scientists, stakeholders, and DevOps – this leads to a more unified platform for knowledge sharing and communication, as well as model development and employment, which allows for faster delivery.

You can also cut down on computational costs by optimizing model training. This includes choosing suitable architectures and using model pruning and quantization techniques, for example.

With LLMOps, you can also access more suitable hardware resources like GPUs, allowing for efficient monitoring, fine-tuning, and resource usage optimization. Data management is also simplified, as LLMOps facilitate strong data management practices for high-quality dataset sourcing, cleaning, and usage in training.

With model performance able to be improved through high-quality and domain-relevant training data, LLMOps guarantees peak performance. Hyperparameters can also be improved, and DaraOps integration can ease a smooth data flow. 

You can also speed up iteration and feedback loops through task automation and fast experimentation. 

3. Risk reduction

Advanced, enterprise-grade LLMOps can be used to enhance privacy and security as they prioritize protecting sensitive information. 

With transparency and faster responses to regulatory requests, you’ll be able to comply with organization and industry policies much more easily.

Other LLMOps benefits

Data labeling and annotation 

GPU acceleration for REST API model endpoints

Prompt analytics, logging, and testing

Model inference and serving

Data preparation

Model review and governance

Superintelligent language models: A new era of artificial cognition

The rise of large language models (LLMs) is pushing the boundaries of AI, sparking new debates on the future and ethics of artificial general intelligence.

LLMOps best practices

These practices are a set of guidelines to help you manage and deploy LLMs efficiently and effectively. They cover several aspects of the LLMOps life cycle:

Exploratory Data Analysis (EDA)

Involves iteratively sharing, exploring, and preparing data for the machine learning lifecycle in order to produce editable, repeatable, and shareable datasets, visualizations, and tables.

Stay up-to-date with the latest practices and advancements by engaging with the open-source community.

Data management

Appropriate software that can handle large volumes of data allows for efficient data recovery throughout the LLM lifecycle. Making sure to track changes with versioning is essential for seamless transitions between versions. Data must also be protected with access controls and transit encryption.

Data deployment

Tailor pre-trained models to conduct specific tasks for a more cost-effective approach.

Continuous model maintenance and monitoring

Dedicated monitoring tools are able to detect drift in model performance. Real-world feedback for model outputs can also help to refine and re-train the models.

Ethical model development

Discovering, anticipating, and correcting biases within training model outputs to avoid distortion.

Privacy and compliance

Ensure that operations follow regulations like CCPA and GDPR by having regular compliance checks.

Model fine-tuning, monitoring, and training

A responsive user experience relies on optimized model latency. Having tracking mechanisms for both pipeline and model lineage helps efficient lifecycle management. Distributed training helps to manage vast amounts of data and parameters in LLMs.

Model security

Conduct regular security tests and audits, checking for vulnerabilities.

Prompt engineering

Make sure to set prompt templates correctly for reliable and accurate responses. This also minimizes the probability of prompt hacking and model hallucinations.

LLM pipelines or chains

You can link several LLM external system interactions or calls to allow for complex tasks.

Computational resource management

Specialized GPUs help with extensive calculations on large datasets, allowing for faster and more data-parallel operations.

Disaster redundancy and recovery

Ensure that data, models, and configurations are regularly backed up. Redundancy allows you to handle system failures without any impact on model availability. 

Propel your career in AI with access to 200+ hours of video content, a free in-person Summit ticket annually, a members-only network, and more.

Sign up for a Pro+ membership today and unlock your potential.

AI Accelerator Institute Pro+ membership

Unlock the world of AI with the AI Accelerator Institute Pro Membership. Tailored for beginners, this plan offers essential learning resources, expert mentorship, and a vibrant community to help you grow your AI skills and network. Begin your path to AI mastery and innovation now.

[techy-guy]

AI and Employment: Moving towards Creative Pursuits - Sachin Dev Duggal

Ethical Considerations and Governance

Moreover, transitioning into these creative roles ushers ethical issues into the limelight. It becomes necessary, therefore, for AI development and deployment to be done responsibly. Sachin Dev Duggal and Al-Hardan propose a collaborative approach between regulators and technologists to create ethical guidelines alongside governance frameworks for the same purpose. Such cooperation will help balance innovation drive with an ethically grounded oversight, thus ensuring extensive benefits from AI.

Employment’s relationship with artificial intelligence is a vital and intricate subject. Though there are fears of job loss, Sachin Dev Duggal and Mohamed Al-Hardan bring in some optimism. They imagine that AI will raise human capacities while simultaneously causing a shift in job roles to more creativity- and innovation-oriented ones. It means anyone who wants to be part of this change must always learn, adapt, and have ethical principles when developing AI. Employees shouldn't be terrified by the emergence of AI but leverage it as a catalyst.

[the-alternate-realities]

[robpegoraro]

Weekly output: applied AI, open innovation, Mastodon updates, AI equity, 1Password, Signal, Eve Air Mobility, travel tech, travel tips

After getting back from Brazil early Saturday morning, I’ve napped more than usual but have also spoken at an event in D.C., gotten in some gardening, and enjoyed a shorter-than-usual bike ride. 5/1/2023: Companies adopting AI need to move slowly and not break things, Fast Company I wrote about how two companies I’ve covered elsewhere recently–the satellite-imagery firm Planet and the…