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Save costs by automatically shutting down idle resources within Amazon SageMaker Studio

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Amazon SageMaker Studio provides a unified, web-based visual interface where you can perform all machine learning (ML) development steps, making data science teams up to 10 times more productive. Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. Studio notebooks are collaborative notebooks that you can launch quickly because you don’t need to set up compute instances and file storage beforehand. Amazon SageMaker is a fully managed service that offers capabilities that abstract the heavy lifting of infrastructure management and provides the agility and scalability you desire for large-scale ML activities with different features and a pay-as-you-use pricing model.

In this post, we demonstrate how to do the following:

  • Detect and stop idle resources that are incurring costs within Studio using an auto-shutdown Jupyter extension that can be both manually and automatically installed
  • Enable event notifications to track user profiles within Studio domains that haven’t installed the auto-shutdown extension
  • Use the installed auto-shutdown extension to manage Amazon SageMaker Data Wrangler costs by automatically shutting down instances that may result in larger than expected costs

Studio components

In Studio, running notebooks are containerized separately from the JupyterServer UI in order to de-couple compute infrastructure sizing. A Studio notebook runs in an environment defined by the following:

  • Instance type – The underlying hardware configuration, which determines the pricing rate. This includes the number and type of processors (vCPU and GPU), and the amount and type of memory.
  • SageMaker image – A compatible container image (either SageMaker-provided or custom) that hosts the notebook kernel. The image defines what kernel specs it offers, such as the built-in Python 3 (Data Science) kernel.
  • SageMaker kernel gateway app – A running instance of the container image on the particular instance type. Multiple apps can share a running instance.
  • Running kernel session – The process that inspects and runs the code contained in the notebook. Multiple open notebooks (kernels) of the same spec and instance type are opened in the same app.

The Studio UI runs as a separate app of type JupyterServer instead of KernelGateway, which allows you to switch an open notebook to different kernels or instance types from within the Studio UI. For more information about how a notebook kernel runs in relation to the KernelGateway app, user, and Studio domain, see Using Amazon SageMaker Studio Notebooks.

Studio billing

There is no additional charge for using Studio. The costs incurred for running Studio notebooks, interactive shells, consoles, and terminals are based on Studio instance type usage. For information about billing along with pricing examples, see Amazon SageMaker Pricing.

When you run a Studio notebook, interactive shell, or image terminal within Studio, you must choose a kernel and an instance type. These resources are launched using a Studio instance based on the chosen type from the UI. If an instance of that type was previously launched and is available, the resource is run on that instance. For CPU-based images, the default instance type is ml.t3.medium. For GPU-based images, the default instance type is ml.g4dn.xlarge. The costs incurred are based on the instance type, and you’re billed separately for each instance. Metering starts when an instance is created, and ends when all the apps on the instance are shut down, or the instance is shut down.

Shut down the instance to stop incurring charges. If you shut down the notebook running on the instance but don’t shut down the instance, you still incur charges. When you open multiple notebooks on the same instance type, the notebooks run on the same instance even if they’re using different kernels. You’re billed only for the time that one instance is running. You can change the instance type from within the notebook after you open it and shut down individual resources, including notebooks, terminals, kernels, apps, and instances. You can also shut down all resources in one of these categories at the same time. When you shut down a notebook, any unsaved information in the notebook is lost. The notebook is not deleted.

You can shut down an open notebook from the Studio File menu or from the Running Terminal and Kernels pane. The Running Terminals and Kernels pane consists of four sections. Each section lists all the resources of that type. You can shut down each resource individually or shut down all the resources in a section at the same time. When you choose to shut down all resources in a section, the following occurs:

  • Running Instances/Running Apps – All instances, apps, notebooks, kernel sessions, Data Wrangler sessions, consoles or shells, and image terminals are shut down. System terminals aren’t shut down. Choose this option to stop the accrual of all charges.
  • Kernel Sessions – All kernels, notebooks, and consoles or shells are shut down.
  • Terminal Sessions – All image terminals and system terminals are shut down.

To shut down resources, in the left sidebar, choose the Running Terminals and Kernels icon. To shut down a specific resource, choose the Power icon on the same row as the resource.

For running instances, a confirmation dialog lists all the resources that will be shut down. For running apps, a confirmation dialog is displayed. Choose Shut Down All to proceed. No confirmation dialog is displayed for kernel sessions or terminal sessions. To shut down all resources in a section, choose the X icon to the right of the section label. A confirmation dialog is displayed. Choose Shut Down All to proceed.

Automatically shut down idle kernels with the JupyterLab extension

Instead of relying on users to shut down resources they’re no longer using, you can use the Studio auto-shutdown extension to automatically detect and shut down idle resources saving costs. JupyterLab extensions are simple add-ons that extend the basic functionality of the notebook environment. The extension automatically shuts down kernels, apps, and instances running within Studio when they’re idle for a stipulated period of time. You can visually configure an idle time threshold (in minutes) via the UI. After the kernels have stayed idle long enough, the extension automatically turns them off. For instructions on how to download and install the extension, see the GitHub repo.

You can install the extension automatically during the startup of the JupyterServer if you’re using AWS Identity and Access Management (IAM) authentication for your users, or do it manually if you’re using single sign-on (SSO) authentication.

After the extension is installed, it shows as an icon in the left sidebar of the Studio interface. You can configure an idle time limit using the user interface this extension provides. Installation instructions are provided in the GitHub repo.

The idle time limit parameter is to set an time after which idle resources with no active notebook sessions are shut down. By default, the idle time limit is set to 120 mins.

Limitations and troubleshooting

The auto-shutdown extension has the following limitations:

  • The extension doesn’t monitor activity on open terminals. For example, if your kernels are idle for the time you had configured but the terminals are not, the extension shuts down the terminals and the kernels.
  • You have to reinstall the extension and configure the idle time limit if you delete the JupyterServer on the SageMaker Studio console and recreate it. This isn’t a limitation if you use the automated installation approach.

You can check the extension logs in Amazon CloudWatch under the /aws/sagemaker/studio log group, and by going through the <Studio_domain>/<user_profile>/JupyterServer/default log stream.

Studio auto-shutdown extension checker

The following diagram illustrates how to enable email notifications to track idle resources running within multiple user profiles residing under a Studio.

Regardless of how you install the auto-shutdown extension in your Studio domain, administrators may want to track and alert any users running without it. To help track compliance and optimize costs, you can follow the instructions in the GitHub repo to set up the auto-shutdown extension checker and enable event notifications.

As per the architecture diagram, a CloudWatch Events rule is triggered on a periodic schedule (for example, hourly or nightly). To create the rule, we choose a fixed schedule and specify how often the task runs. For our target, we choose an AWS Lambda function that periodically checks whether all user profiles under a Studio domain have installed the extension for auto-shutdown or not. This function collects user profiles names that have failed to meet this requirement.

The user profiles are then routed to an Amazon Simple Notification Service (Amazon SNS) topic that Studio admins and other stakeholders can subscribe to in order to get notifications (such as via email or Slack). The following screenshot shows an email alert notification in which the user profiles user-w and user-y within the SageMaker domain d-bo6udbiz4vmi haven’t installed the auto-shutdown extension.

Auto-shutdown Data Wrangler resources

To further demonstrate how the auto-shutdown extension works, let’s look at it from the perspective of Data Wrangler within Studio. Data Wrangler is a new capability of SageMaker that makes it faster for data scientists and engineers to prepare data for ML applications by using a visual interface.

When you start Data Wrangler from Studio, it automatically spins up an ml.m5.4xlarge instance and starts the kernel using that instance. When you’re not using Data Wrangler, it’s important to shut down the instance on which it runs to avoid incurring additional fees.

Data Wrangler automatically saves your data flow every 60 seconds. To avoid losing work, save your data flow manually before shutting Data Wrangler down. To do so, choose File and then choose Save Data Wrangler Flow.

To shut down the Data Wrangler instance in Studio, choose the Running Instances and Kernels icon. Under Running Apps, locate the sagemaker-data-wrangler-1.0 app. Choose the Power icon next to this app.

Following these steps manually can be cumbersome, and it’s easy to forget. With the auto-shutdown extension, you can ensure that idle resources powering Data Wrangler are shut down cautiously to avoid extra SageMaker costs.

Conclusion

In this post, we demonstrated how to reduce SageMaker costs by using an auto-shutdown Jupyter extension to shut down idle resources running within Studio. We also showed how to set up an auto-shutdown extension checker and enable event notifications to track user profiles within Studio who haven’t installed the extension. Finally, we showed how the extension can reduce Data Wrangler costs by shutting down idle resources powering Data Wrangler.

For more information about optimizing resource usage and costs, see Right-sizing resources and avoiding unnecessary costs in Amazon SageMaker.

If you have any comments or questions, please leave them in the comments section.


About the Authors

Arunprasath Shankar is an Artificial Intelligence and Machine Learning (AI/ML) Specialist Solutions Architect with AWS, helping global customers scale their AI solutions effectively and efficiently in the cloud. In his spare time, Arun enjoys watching sci-fi movies and listening to classical music.

 Andras Garzo is a ML Solutions Architect in the AWS AI Platforms team and helps customers to migrate to SageMaker, adopt best practices and save cost.

Pavan Kumar Sunder is a Senior R&D Engineer with Amazon Web Services. He provides technical guidance and helps customers accelerate their ability to innovate through showing the art of the possible on AWS. He has built multiple prototypes around AI/ML, IoT, and Robotics for our customers.

Alex Thewsey is a Machine Learning Specialist Solutions Architect at AWS, based in Singapore. Alex helps customers across Southeast Asia to design and implement solutions with AI and ML. He also enjoys karting, working with open source projects, and trying to keep up with new ML research.

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Source: https://aws.amazon.com/blogs/machine-learning/save-costs-by-automatically-shutting-down-idle-resources-within-amazon-sagemaker-studio/

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A Brief Intro to the GPT-3 Algorithm

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Generative Pre-trained Transformer 3 (GPT-3) embraces and augments the GPT-2 model architecture, including pre-normalization, modified initialization, and reversible tokenization. It exhibits strong performance on many Natural Language Processing (NLP) tasks.

GPT-3 is an auto-regressive artificial intelligence algorithm developed by OpenAI, an AI-powered research laboratory located in San Francisco, California.

It is a massive artificial neural network that takes help from deep learning to generate human-like text and is trained on huge text datasets with thousands of billions of words. It is the third-generation AI language prediction model in the GPT-n series and the successor to GPT-2.

In simple words, OpenAI GPT-3 was fed inputs the ways how billions of people write and also was taught how to pick up on writing patterns based on user entry. Once few inputs are offered, the model will generate intelligent text following the submitted pattern and structure. It is also the largest AI language algorithm that produces billions of words a day.

GPT-3 working process

This artificial intelligence algorithm is a program that can calculate the word or even the character which must appear in a text given in relation to the words around it. This is called the conditional probability of words. It is a generative neural network that allows out a numeric score or a yes or no answer. It also generates long sequences of the original text as its output.

The total number of weights the OpenAI GPT-3 dynamically holds in its memory and utilizes to process every query is 175 billion.

Examples

•noun + verb = subject + verb
• noun + verb + adjective = subject + verb + adjective
• verb + noun = subject + verb
• noun + verb + noun = subject + verb + noun
• noun + noun = subject + noun
• noun + verb + noun + noun = subject + verb + noun + noun

The stream of algorithmic content in GPT-3

Every month over 409 million people view more than 20 billion pages, and users publish around 70 million posts on WordPress, which is the dominant content management system online.

The main specialty of OpenAI GPT-3 is the capacity to respond intelligently to minimal input. It is extensively trained on billions of parameters and produces up to 50,000 characters without any supervision. This one-of-a-kind AI neural network generates texts at an amazing quality, making it quite tough for a normal human to understand whether the output was written by GPT-3 or a human.

Training of the GPT-3

The training of the GPT-3 artificial intelligence algorithm has two steps.

Step – 1: It needs to create the vocabulary, production rules, and the various categories. It can be achieved by offering inputs in the form of books. For each word, the model predicts the category to that the word belongs, and afterward, a production rule should be built.

• Step – 2: The development of the vocabulary and production rules for each category takes place. This can be achieved by offering the inputs to the model with sentences. For every sentence, the model will be predicting the category to which each word belongs, and after that, a production rule should be built.

The model consists of a few tricks that allow it a provision to boost its capability to generate texts. For example, it can guess the inception of a word by understanding the context of the word. It also predicts the next word depending on the last word of a sentence. It can also predict the length of a sentence.

Conclusion

There’s a lot of hype for the GPT-3 AI algorithm right now. One can say that in the future, it will be offering more beyond the text that includes pictures, videos, and many more. Many researchers also predicted that GPT-3 would possess the capability to translate words to pictures and pictures to words.

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How AI Is Catapulting Cannabis into the Future

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John Kaweske Hacker Noon profile picture

@johnkaweskeJohn Kaweske

John Kaweske is Founder & CEO of North Star Holdings, Inc. and Tweedleaf.

We like to think we know a thing or two about artificial intelligence. We’ve seen the ominous technological future depicted in television shows and films of robots slowly amalgamating into society. But this imagery is all wrong. AI isn’t taking over the world in the form of lifelike robots. Instead, automation has been in our lives for quite some time now, and many of us are likely not even aware of it.

Driverless cars. Voice-activated home assistants. Smartphones. These are all made possible because of artificial intelligence. It’s not only changed our lives in unimaginable ways, but it’s also allowed us to collect, analyze, and interpret data that can help us better understand the world around us. And for business owners, that’s critical not only for our organizational efficacy and profitability, but it allows us to see our customers through an entirely new lens.

It’s no surprise that many industries are already taking advantage of artificial intelligence in their practices — and now, so is the cannabis industry. The legal cannabis market is predicted to reach over $66 billion by 2025. To prepare for this growth, leaders in the cannabis sector must exploit AI’s transformative powers or risk falling behind the competition. 

So, what exactly are some of the transformative powers artificial intelligence holds in the cannabis industry? Let’s find out.

Enhanced cultivation capabilities

Have you ever enjoyed the benefits of smart home technology? If you have, you know that smart lights and smart thermostats allow you to control your home’s lighting and temperature from anywhere in the world. Growers can now enjoy these same benefits. By using artificial intelligence, we are better able to manage our crops, which can generate higher yields at lower prices. This is what we do at Tweedleaf.

We use AI to adjust the pH level and moisture levels of our soil. We also use AI to help monitor and control lighting exposure to ensure our plants receive the appropriate level of photosynthesis. We even use it for pest control. By giving growers real-time updates, AI eliminates the ‘guessing game’ of cultivation. Is the growth rate slower than usual? Are nutrient levels low? Is there a pest infestation? Artificial intelligence alerts growers to any issues, so they know exactly what to fix and how to fix it.

All of this is pretty miraculous when you think about it. AI allows growers to create the perfect environment for plants rather than leaving it up to chance. Growers shouldn’t have to hope for the best; they can make this ‘best’ their reality.

But one of the biggest advantages of artificial intelligence is that it makes it possible for growers to create and breed new, customized strains. As the legal marijuana market continues to expand, access to a variety of strains ensures that all consumers can benefit from the remarkable and healing powers of cannabis. And once growers perfect their new strains, they can then use AI to lock in the correct watering, lighting, and temperature schedules that will aid in the cultivation and production of a diverse range of plants.

Recommendation apps

For many of us, our smartphones were our earliest introductions to artificial intelligence. We’ve become so accustomed to their convenience that we can’t imagine our lives without them. This is exactly why they were created — to make our lives better.

Think about the last time you shopped online. As you were shopping, the store probably sent you some ‘recommended’ items to view. Were they scarily accurate? This wasn’t by mistake. By using AI, brands can analyze your preferences and interests and pull items from their store they also think you’d like. While this may seem a bit eerie at first, we’ve eventually come to love these recommendations. Instead of spending hours browsing a site, the brands are doing the heavy lifting for us and pulling the products we’ll have the most interest in.

The cannabis industry can take advantage of these same benefits. Certain apps like Uppy can help medical marijuana users traverse the world of legal cannabis. What experience do you hope to get from a cannabis product? Do you use it to alleviate a physical injury? Do you need it to help you sleep better at night? Are you a creative person and want to gain some inspiration? Recommendation apps can pull information about you and use it to offer insight into new strains you might like to try.

This sort of capability is so invaluable because it enriches our experience and can transform our lives. 

Better customer experience/service

While artificial intelligence has many benefits for companies, its central mission is improving the customer experience. 

If you’ve ever browsed a marijuana company’s website and interacted with a chatbot, there’s a good chance you spoke with a digital budtender that was powered by AI. And chances are you probably didn’t even realize you weren’t talking to a real person.

This is how powerful artificial intelligence has become. And now, over half of people would rather speak with a chatbot than a human because it saves time, and these chatbots can often be more knowledgeable. 

Consumers — especially new cardholders — often have a lot of questions about different products at the onset yet are too intimidated and embarrassed to walk into a dispensary and approach an employee for answers. Digital budtenders can help walk people through these questions online while also providing them with insight into the cannabis products that will best fit their needs. As customers feel more comfortable, they will begin interacting with your company more and more, which will result in a higher number of sales. 

Additionally, brands can also take advantage of tools like QR codes to have this same impact in-store. By placing a QR code on your packaging, a dispensary can equip customers with all of the information they should know about a product: potency, expected effects, user reviews, lab testing, etc. And because they don’t have to approach a person with all their questions, this could also lead to more sales.

Artificial intelligence isn’t just a revolutionary technology; it’s the future of business. Companies that implement this transformative technology do so not only to the benefit of their organizations but also to their customers. 

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Extra Crunch roundup: influencer marketing 101, spotting future unicorns, Apple AirTags teardown

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With the right message, even a small startup can connect with established and emerging stars on TikTok, Instagram and YouTube who will promote your products and services — as long as your marketing team understands the influencer marketplace.

Creators have a wide variety of brands and revenue channels to choose from, but marketers who understand how to court these influencers can make inroads no matter the size of their budget. Although brand partnerships are still the top source of revenue for creators, many are starting to diversify.

If you’re in charge of marketing at an early-stage startup, this post explains how to connect with an influencer who authentically resonates with your brand and covers the basics of setting up a revenue-share structure that works for everyone.


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Our upcoming TC Early Stage event is devoted to marketing and fundraising, so expect to see more articles than usual about growth marketing in the near future.

We also ran a post this week with tips for making the first marketing hire, and Managing Editor Eric Eldon spoke to growth leader Susan Su to get her thoughts about building remote marketing teams.

We’re off today to celebrate the Juneteenth holiday in the United States. I hope you have a safe and relaxing weekend.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist

As the economy reopens, startups are uniquely positioned to recruit talent

Little Fish in Form of Big Fish meeting a fish.

Image Credits: ballyscanlon (opens in a new window) / Getty Images

The pandemic forced a reckoning about the way we work — and whether we want to keep working in the same way, with the same people, for the same company — and many are looking for something different on the other side.

Art Zeile, the CEO of DHI Group, notes this means it’s a great time for startups to recruit talent.

“While all startups are certainly not focused on being disruptive, they often rely on cutting-edge technology and processes to give their customers something truly new,” Zeile writes. “Many are trying to change the pattern in their particular industry. So, by definition, they generally have a really interesting mission or purpose that may be more appealing to tech professionals.”

Here are four considerations for high-growth company founders building their post-pandemic team.

Refraction AI’s Matthew Johnson-Roberson on finding the middle path to robotic delivery

Matthew Johnson-roberson

Image Credits: Bryce Durbin

“Refraction AI calls itself the Goldilocks of robotic delivery,” Rebecca Bellan writes. “The Ann Arbor-based company … was founded by two University of Michigan professors who think delivery via full-size autonomous vehicles (AV) is not nearly as close as many promise, and sidewalk delivery comes with too many hassles and not enough payoff.

“Their ‘just right’ solution? Find a middle path, or rather, a bike path.”

Rebecca sat down with the company’s CEO to discuss his motivation to make “something that is useful to the general public.”

How to identify unicorn founders when they’re still early-stage

Image Credits: RichVintage (opens in a new window)/ Getty Images

What are investors looking for?

Founders often tie themselves in knots as they try to project qualities they hope investors are seeking. In reality, few entrepreneurs have the acting skills required to convince someone that they’re patient, dedicated or hard working.

Johan Brenner, general partner at Creandum, was an early backer of Klarna, Spotify and several other European startups. Over the last two decades, he’s identified five key traits shared by people who create billion-dollar companies.

“A true unicorn founder doesn’t need to have all of those capabilities on day one,” Brenner, writes “but they should already be thinking big while executing small and demonstrating that they understand how to scale a company.”

Founders Ben Schippers and Evette Ellis are riding the EV sales wave

disrupt mobility roundup

Image Credits: TechCrunch

EV sales are driving demand for services and startups that fulfill the new needs of drivers, charging station operators and others.
Evette Ellis and Ben Schippers took to the main stage at TC Sessions: Mobility 2021 to share how their companies capitalized on the new opportunities presented by the electric transportation revolution.

Scale AI CEO Alex Wang weighs in on software bugs and what will make AV tech good enough

Image Credits: Alexandr Wang

Scale co-founder and CEO Alex Wang joined us at TechCrunch Sessions: Mobility 2021 to discuss his company’s role in the autonomous driving industry and how it’s changed in the five years since its founding.

Scale helps large and small AV players establish reliable “ground truth” through data annotation and management, and along the way, the standards for what that means have shifted as the industry matures.

Even if two algorithms in autonomous driving might be created more or less equal, their real-world performance could vary dramatically based on what they’re consuming in terms of input data. That’s where Scale’s value prop to the industry starts, and Wang explains why.

Edtech investors are flocking to SaaS guidance counselors

Image Credits: Getty Images / Vertigo3d

The prevailing post-pandemic edtech narrative, which predicted higher ed would be DOA as soon as everyone got their vaccine and took off for a gap year, might not be quite true.

Natasha Mascarenhas explores a new crop of edtech SaaS startups that function like guidance counselors, helping students with everything from study-abroad opportunities to swiping right on a captivating college (really!).

“Startups that help students navigate institutional bureaucracy so they can get more value out of their educational experience may become a growing focus for investors as consumer demand for virtual personalized learning increases,” she writes.

Dear Sophie: Is it possible to expand our startup in the US?

lone figure at entrance to maze hedge that has an American flag at the center

Image Credits: Bryce Durbin/TechCrunch

Dear Sophie,

My co-founders and I launched a software startup in Iran a few years ago, and I’m happy to say it’s now thriving. We’d like to expand our company in California.

Now that President Joe Biden has eliminated the Muslim ban, is it possible to do that? Is the pandemic still standing in the way? Do you have any suggestions?

— Talented in Tehran

Companies should utilize real-time compensation data to ensure equal pay

Two women observing data to represent collecting data to ensure pay equity.

Image Credits: Rudzhan Nagiev (opens in a new window) / Getty Images

Chris Jackson, the vice president of client development at CompTrak, writes in a guest column that having a conversation about diversity, equity and inclusion initiatives and “agreeing on the need for equality doesn’t mean it will be achieved on an organizational scale.”

He lays out a data-driven proposal that brings in everyone from directors to HR to the talent acquisition team to get companies closer to actual equity — not just talking about it.

Investors Clara Brenner, Quin Garcia and Rachel Holt on SPACs, micromobility and how COVID-19 shaped VC

tc sessions mobility speaker_investorpanel-1

Image Credits: TechCrunch

Few people are more closely tapped into the innovations in the transportation space than investors.

They’re paying close attention to what startups and tech companies are doing to develop and commercialize autonomous vehicle technology, electrification, micromobility, robotics and so much more.

For TC Sessions: Mobility 2021, we talked to three VCs about everything from the pandemic to the most overlooked opportunities within the transportation space.

Experts from Ford, Toyota and Hyundai outline why automakers are pouring money into robotics

disrupt mobility roundup

Image Credits: TechCrunch

Automakers’ interest in robotics is not a new phenomenon, of course: Robots and automation have long played a role in manufacturing and are both clearly central to their push into AVs.

But recently, many companies are going even deeper into the field, with plans to be involved in the wide spectrum of categories that robotics touch.

At TC Sessions: Mobility 2021, we spoke to a trio of experts at three major automakers about their companies’ unique approaches to robotics.

Apple AirTags UX teardown: The trade-off between privacy and user experience

Image Credits: James D. Morgan/Getty Images

Apple’s location devices — called AirTags — have been out for more than a month now. The initial impressions were good, but as we concluded back in April: “It will be interesting to see these play out once AirTags are out getting lost in the wild.”

That’s exactly what our resident UX analyst, Peter Ramsey, has been doing for the last month — intentionally losing AirTags to test their user experience at the limits.

This Extra Crunch exclusive helps bridge the gap between Apple’s mistakes and how you can make meaningful changes to your product’s UX.

How to launch a successful RPA initiative

3D illustration of robot humanoid reading book in concept of future artificial intelligence and 4th fourth industrial revolution . (3D illustration of robot humanoid reading book in concept of future artificial intelligence and 4th fourth industrial r

Image Credits: NanoStockk (opens in a new window) / Getty Images

Robotic process automation (RPA) is no longer in the early-adopter phase.

Though it requires buy-in from across the organization, contributor Kevin Buckley writes, it’s time to gather everyone around and get to work.

“Automating just basic workflow processes has resulted in such tremendous efficiency improvements and cost savings that businesses are adapting automation at scale and across the enterprise,” he writes.

Long story short: “Adapting business automation for the enterprise should be approached as a business solution that happens to require some technical support.”

Mobility startups can be equitable, accessible and profitable

tc sessions

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Mobility should be a right, but too often it’s a privilege. Can startups provide the technology and the systems necessary to help correct this injustice?

At  our TC Sessions: Mobility 2021 event, we sat down with Revel CEO and co-founder Frank Reig, Remix CEO and co-founder Tiffany Chu, and community organizer, transportation consultant and lawyer Tamika L. Butler to discuss how mobility companies should think about equity, why incorporating it from the get-go will save money in the long run, and how they can partner with cities to expand accessible and sustainable mobility.

CEO Shishir Mehrotra and investor S. Somasegar reveal what sings in Coda’s pitch doc

Image Credits: Carlin Ma / Madrona Venture Group/Brian Smale

Coda CEO Shishir Mehrotra and Madrona partner S. Somasegar joined Extra Crunch Live to go through Coda’s pitch doc (not deck. Doc) and stuck around for the ECL Pitch-off, where founders in the audience come “onstage” to pitch their products to our guests.

Extra Crunch Live takes place every Wednesday at 3 p.m. EDT/noon PDT. Anyone can hang out during the episode (which includes networking with other attendees), but access to past episodes is reserved exclusively for Extra Crunch members. Join here.

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AI-driven hedge fund rules out Bitcoin for lack of ‘fundamentals’

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A Swedish hedge fund that returned roughly four times the industry average last year using artificial intelligence won’t touch Bitcoin, based on an assessment that the cryptocurrency doesn’t lend itself to sensible analysis.

Photo by Bloomberg Mercury

Patrik Safvenblad, the chief investment officer of Volt Capital Management AB, says the problem with Bitcoin and other crypto assets is that they “do not have accessible fundamentals that we could build a model on.”

“When there is a crisis, markets generally move toward fundamentals. Not the old fundamentals but new, different fundamentals,” he said in an interview. So if an asset doesn’t provide that basic parameter, “we stay away from that,” he said.

The role of Bitcoin in investment portfolios continues to split managers, as the world’s most popular cryptocurrency remains one of its most volatile asset classes. One coin traded at less than $40,000 on Friday, compared with an April peak of $63,410. This time last year, a single Bitcoin cost around $10,000.

Among Volt’s best-known investors is Bjorn Wahlroos, the former Nordea Bank Abp chairman. His son and former professional poker player, Thomas Wahlroos, is Volt’s board chairman. The fund currently manages assets worth just $73 million, on which it returned 41% in 2020, four times the industry average.

Bitcoin enthusiasts recently received a boost when hedge fund manager Paul Tudor Jones told CNBC he likes it “as a portfolio diversifier.” He went on to say that the “only thing” he’s “certain” about is that he wants “5% in gold, 5% in Bitcoin, 5% in cash, 5% in commodities.”

Meanwhile, Bank of America Corp. research shows that Bitcoin is about four times as volatile as the Brazilian real and Turkish lira. And the International Monetary Fund has warned that El Salvador’s decision to adopt Bitcoin as legal tender “raises a number of macroeconomic, financial and legal issues that require very careful analysis.”

Safvenblad says it’s more than just a matter of Bitcoin’s lack of fundamentals. He says he’s not ready to hold an asset that’s ultimately designed to dodge public scrutiny.

Volt would “much prefer to be in a regulated market with regulated trading,” he said. “And Bitcoin is not yet fully regulated.”

The hedge-fund manager has chosen 250 models it thinks will make money, and its AI program then allocates daily weightings. Volt’s investment horizon is relatively short, averaging about 10-12 trading days. It holds roughly 60 positions at any given time, and its current analysis points toward what Safvenblad calls a “nervous long.”

“In the past few weeks the program has turned more bearish,” he said. We have some positions that anticipate a slowdown, for example long fixed-income, and the models have now trimmed our long positions in commodities. Today, the portfolio reflects a more balanced outlook.”

Safvenblad says the advantage to Volt’s AI model is that it’s unlikely to miss any signals. “We don’t say that we know where the world is heading. But we have a system that monitors everything that could mean something.”

— Jonas Cho Walsgard, Bloomberg Mercury

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://bankautomationnews.com/allposts/wealth/ai-driven-hedge-fund-rules-out-bitcoin-for-lack-of-fundamentals/

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