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AI is no longer the exclusive domain of large enterprises. It’s entering the business mainstream—but it’s successful only sometimes, says Salesforce President and Chief Operating Officer Bret Taylor. A thoughtful AI strategy should be customer-focused, with clearly defined business outcomes, and the key to all of this is data. In his talk at the <a href="https://scale.com/events/transformx" rel="noopener noreferrer" target="_blank">Scale TransformX 2021 conference</a>, Taylor discussed the key considerations that businesses of all sizes should keep in mind to ensure that they delight their customers and achieve sustainable business outcomes. <iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://fast.wistia.com/embed/medias/42dyrzqs4c"></iframe> Here’s a summary of the seven key takeaways from Taylor’s presentation. For more details, watch the session video above. <h1>Business Today Is All About the Data</h1>“If you don't have a digital business, you don't have a business,” says Taylor. Enterprises need to maintain constant engagement with customers, supply chains, partners, and employees, in granular detail. Each of these interactions produces data that businesses can use to create more intelligent and personalized relationships. This <a href="https://insidebigdata.com/2017/02/16/the-exponential-growth-of-data/" rel="noopener noreferrer" target="_blank">growing tsunami of data</a> requires intelligent algorithms and automation to make sense of it all. As Marc Andreessen, co-founder of venture capital firm Andreessen Horowitz, famously said, <a href="https://a16z.com/2011/08/20/why-software-is-eating-the-world/" rel="noopener noreferrer" target="_blank">“Software is eating the world.”</a> Today, however, it’s AI that has become central to everything that a company such as Salesforce does. <h1>Build a Data Culture</h1>AI and deep learning can’t be the exclusive domain of technology companies. To get there, every company must strive to <a href="https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/Why%20data%20culture%20matters/Why-data-culture-matters.ashx" rel="noopener noreferrer" target="_blank">develop a data culture</a> that allows employees to see and understand the flow of information moving through the business. A <a href="https://www.gartner.com/smarterwithgartner/do-you-need-a-chief-data-officer" rel="noopener noreferrer" target="_blank">chief data officer (CDO)</a> should act as a steward for all that information, without trapping data in internal silos. Instead, everyone needs to be empowered to make data-informed decisions based on objective views of customers, supply chains, and the flow of resources. Ultimately, companies that create open data cultures will easily outcompete those that don’t. <h1>Ethical AI Builds Trust</h1>Shifting toward a data culture requires building in <a href="https://www.salesforce.com/blog/what-is-ethics-by-design/" rel="noopener noreferrer" target="_blank">ethics by design</a>, in much the same way that enterprises have incorporated <a href="https://patchstack.com/security-design-principles-owasp/" rel="noopener noreferrer" target="_blank">security by design</a>. To deliver an ethical deployment of AI, the number one value must be trust. With that in mind, Salesforce created the position of <a href="https://www.cnbc.com/2018/12/10/salesforce-hires-its-first-chief-ethical-and-humane-use-officer.html" rel="noopener noreferrer" target="_blank">chief ethical and humane use officer</a> to guide the company’s adoption of AI. Salesforce wanted to ensure that all of its AI is responsible, accountable, transparent, empowering, and inclusive. Taylor contends that failing to take this into account from the start inevitably leads to unintended negative consequences and general degradation of trust in technology. Society is now holding software and AI to a higher standard. Companies that develop AI today must embrace conversations about ethics and trust, and they must embed those conversations into their development processes. Building trust also requires bringing in stakeholders who aren’t data scientists to help shape the responsible use of these technologies. <h1>Focus on Strategy, Not AI for AI’s Sake</h1>While every company needs to become an AI company to remain competitive, too many organizations are succumbing to the hype about digital transformation without having <a href="https://www.thetilt.com/content/stages-of-digital-transformation" rel="noopener noreferrer" target="_blank">clear goals</a> in mind. According to the <a href="https://www.gartner.com/en/information-technology/insights/cio-agenda" rel="noopener noreferrer" target="_blank">2021 Gartner CIO Agenda survey</a>, while 94% of CIOs are working to deploy AI, only a quarter have done so successfully. “Don’t start with the technology; start with customers,” Taylor says. Focus on their experience and what your customers need and want. Clarify the project’s scope and look for the easy wins first. “If AI becomes the purpose of the project, it becomes a bottomless pit of investment,” he says. <h1>One Shortcut to Good AI: Don’t Build It; Buy It</h1>Salesforce’s <a href="https://www.salesforce.com/products/einstein/faq/" rel="noopener noreferrer" target="_blank">AI platform Einstein</a> is a prime example of Salesforce focusing on building AI technology that is customer-focused and that “can be turned on with clicks, not code,” says Taylor. Salesforce customer <a href="https://www.salesforce.com/blog/orvis-ai-customer-engagement/" rel="noopener noreferrer" target="_blank">Orvis uses Einstein</a> to do simple analyses of the potential engagement of its marketing messages prior to their publication. That approach increased Orvis’ click-through rate by 22% and boosted traffic by 30%. This is one way an enterprise can deploy customer-focused experiences with the help of AI, without having to invest in the substantial skills and technology normally associated with developing it. <h1>Align Your AI Investment with Business Priorities</h1>A common key strategy, especially for B2C enterprises, starts with identifying use cases that increase personalization and automation, thus streamlining the customer experience. Then, identify the data and AI tools that could help create those use cases. For its part, Orvis’ goal is to keep its support calls short, and <a href="https://www.salesforce.com/news/press-releases/2019/11/19/hey-einstein-salesforce-brings-voice-to-every-customer-experience/" rel="noopener noreferrer" target="_blank">Einstein can help by transcribing and analyzing customer questions</a> and suggesting answers to call center personnel in real time. The lesson here: Bring in just a few machine learning experts and combine them with off-the-shelf SaaS platforms to discover unique value in AI that can be customized around your business. <h1>Don’t Forget the Quick Wins</h1>Getting value from AI isn’t science fiction, Taylor says. Simply step back and focus on the low-hanging fruit to achieve tangible business outcomes. <h2>Learn More</h2> To learn more about how Salesforce built its AI strategy and lessons learned, <a href="https://exchange.scale.com/public/videos/enterprise-ai-strategy-bret-taylor-on-lessons-learned-at-salesforce" rel="noopener noreferrer" target="_blank">watch Taylor’s talk and view the full transcript here</a>.

A thoughtful AI strategy should be customer-focused, with clearly defined business outcomes, and the key to all of this is data. 

Effective AI Depends on Good Data, With Clearly Defined Business Outcomes Focused on Customers - Transformx 2021 Conference - Scale AI

7 Elements of a Well-Thought-Out AI Strategy: Salesforce COO Bret Taylor

Dr. Li shares explores how recent AI research helps robots  perform both short-horizon and long-horizon tasks.

Dr. Fei-Fei Li On Teaching Robots Complex Tasks - TransformX 2021 Conference - Scale AI

On Teaching Robots To Perform Complex Tasks with Dr. Fei-Fei Li 

Today we are excited to welcome SiaSearch, a leading data management platform, to Scale. We acquired SiaSearch, a spin-off of the European AI venture studio Merantix, to enable us to expand Scale’s suite of tools for every AI team, broaden our footprint in European markets, and deepen the technical expertise of the company. We know that the autonomous vehicle (AV) industry is one of the best breeding grounds for AI technology. SiaSearch’s Berlin-based team brings experienced, high-caliber engineers from the European AV industry to Scale, which will help us deliver world-class AI solutions to support the complete machine learning lifecycle. The SiaSearch team will join Scale's Nucleus business unit as we grow our platform to support more data types and use cases across a wide array of industries and markets. SiaSearch’s unique interval querying engine, which is inspired by algorithms originally designed for large-scale analysis of DNA sequences, allows for lightning-fast search across massive video and LiDAR datasets and complements what Scale is building with <a href="https://studio.plasmic.app/scale.com/nucleus" rel="noopener noreferrer" target="_blank">Nucleus</a>. We are excited to weave the SiaSearch technology into the Nucleus product, accelerating development and improving the developer user experience. We see even more potential down the line to incorporate SiaSearch’s world-class team and technology throughout our entire suite of products. Merantix and the SiaSearch team have a strong network in Europe and have partnered with the world’s leading automakers, including Porsche and Volkswagen. We’re thrilled to expand Scale’s footprint to Germany and look forward to working with SiaSearch’s customers and networks to significantly increase our ability to serve European-based customers. We’re excited for what the future holds with this latest addition to our team, and we’re always looking for new opportunities. If you are interested in joining this incredible team, check out our <a href="https://scale.com/careers#jobs" rel="noopener noreferrer" target="_blank">open positions</a>. Or, if you’re interested in partnering with us, please <a href="https://scale.com/sales" rel="noopener noreferrer" target="_blank">reach out</a>.

Scale has acquired SiaSearch to further enhance Nucleus, its data management platform.

The SiaSearch team and Nucleus will collaborate on Dataset Managment for Nucleus.

SiaSearch Acquisition: Delivering the Full Data Engine to Our Customers

Waymo’s co-CEO Dmitri Dolgov shares Waymo’s approach to building powerful machine learning models for self-driving vehicles

Dmitri Dolgov of Waymo Discusses Data-centric AI - TransformX 2021 Conference - Scale AI

Dmitri Dolgov of Waymo Discusses Data-centric AI

The co-founder of Schmidt Futures and a former CEO of Google, discusses how AI will shape our global future. Eric was joined by Scale AI CEO Alexandr Wang

Eric Schmidt Discusses the Geopolitics of AI - TransformX 2021 Conference - Scale AI

Eric Schmidt Discusses the Geopolitics of AI

Justin Basilico, Director of Machine Learning and Recommender Systems at Netflix describes how ‘everything at Netflix is a recommendation’.

Netflix on Recommendations and Personalization - TransformX 2021 Conference - Scale AI

Netflix Explains Recommendations and Personalization

Clement Delangue HuggingFace CEO, shares what Transformers can do. He explores how we might ensure they are used ethically and transparently.

Clement Delangue HuggingFace CEO on Transformers - TransformX 2021 Conference - Scale AI

HuggingFace CEO, Clement Delangue: Are Transformers Becoming the Most Impactful Tech of the Decade?

Today we’re thrilled to share that Nucleus now supports 3D Sensor Fusion data, including LiDAR point clouds. <a href="https://scale.com/nucleus" rel="noopener noreferrer" target="_blank">Scale Nucleus</a> is the mission control for your data, providing advanced tooling for curating, visualizing and exploring machine learning datasets and debugging ML models. We recently launched an all-new Nucleus for dataset management, complete with privacy features and updated Autotag capabilities, all to help teams build better ML models by building better datasets. Now, Nucleus can help teams manage 3D datasets to develop highly accurate perception models for a range of use cases, including autonomous vehicles, robotics, and augmented/virtual reality (AR and VR). Scale Nucleus helps you visualize and debug 3D Sensor Fusion data by making it easy to: <ul><li> </li><li>Construct Scenes that contain a sequence of LiDAR point clouds and their corresponding camera images over time</li><li> </li><li> </li><li>Visualize, query and share sensor fusion data with colleagues </li><li> </li><li> </li><li>Integrate Sensor Fusion data with your 3D model predictions to uncover and identify:</li><li> </li><li>Discrepancies between 3D object detection model outputs and ground truth</li><li class="ql-indent-1"> </li><li> </li><li>False positive, common class confusions, and cuboid predictions that have low Intersection over Union (IoU) with ground truth</li><li class="ql-indent-1"> </li><li> </li><li class="ql-indent-1">Model performance and improvements over time</li><li> </li></ul>The Nucleus API makes it easy to import your data, construct scenes, and add model predictions. You can also import data from an existing Scale project directly into Nucleus. <h2>A Primer: Nucleus Scenes</h2> Scenes are the building blocks of 3D datasets in Nucleus and are designed with point clouds as the primary unit of data. More formally, a Scene is a sequence of Frames over time, where each Frame contains exactly one point cloud and an optional set of camera images. Here we can see a LiDAR point cloud above two paired images (the full dataset includes more than just front and back cameras, but a full suite of sensor data), which helps explain why we designed Nucleus Scenes to encapsulate data across both time and sensor modalities: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/00157f16-6ed9-4ffc-45cc-60b676806e00/public" alt="Structure Diagram of Nucleus 3D Scenes">One useful way to interpret a LiDAR Scene is as a table of sensor readings. A row in the table corresponds to a sequence of readings from a particular sensor, while a column in the table represents a collection of sensor data from the same timestamp (we call this a “frame” of the scene).<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/6ea98ea3-b4d4-40eb-f36f-3562514cab00/public" alt="Complex Nucleus 3D Scene on SF's Embarcadero, Quarter Angle View">On their own, point clouds can be difficult to interpret, due to lack of motion cues or associated camera data. Nucleus Scenes make sets of 3D and 2D data easier to interpret for the following reasons: first, any number of 2D camera frames can be attached to each 3D LiDAR frame; and second, Scenes link frames from the same timespan so that you can examine all frames in a linear fashion. Let’s now view the same Scene in Nucleus and see if we can identify the mystery object from above:<iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://scaleai.wistia.com/embed/medias/8ikm69knof"></iframe> After uploading predicted cuboids from an ML model for your Scene, Nucleus automatically computes performance metrics under the hood. You can sort predicted cuboids by IoU or filter by false positives to quickly narrow in on your model’s failure modes. (These features have been available for 2D image analysis for some time, but today we are announcing their ability for 3D data, too.) To summarize, by creating and uploading 3D Scenes to Nucleus, you can easily visualize LiDAR point clouds and images side-by-side, quality-check your ground truth annotations, and search your data based on metadata or even ML-produced attributes. Lastly, you can use Nucleus to measure key prediction performance metrics, such as IoU and false positives.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/6908e9df-249d-47df-4db5-a39827556c00/public" alt="Birds' Eye View of SF Embarcadero as Seen through LiDAR and Multiple Image Sensors in Nucleus Scene"><h2>Getting Started with 3D Data in Nucleus</h2> Visualizing, querying, and measuring the quality of sensor fusion datasets is a challenge facing even the most sophisticated ML teams. With the newly launched support of multimodal scenes, Nucleus makes it easy to navigate through massive datasets, and quickly narrow down on the most difficult examples. Unleash the full potential of your datasets by leveraging Nucleus for debugging, search, and quality related workflows. The complete code for this tutorial is available in <a href="https://colab.research.google.com/github/brwillia/nuc-3d/blob/main/nucleus_3d_demo.ipynb#scrollTo=rr88VqEHaL2_" rel="noopener noreferrer" target="_blank">this Colab notebook</a>. If you’d like to follow along step by step and create a personal dataset on Nucleus, you will first need to make a <a href="http://scale.com/signup" rel="noopener noreferrer" target="_blank">free Scale account</a> and obtain a test API key (which you can <a href="https://dashboard.scale.com/settings/profile" rel="noopener noreferrer" target="_blank">find here</a> if you navigate to your user icon, and then click on API keys). You can then copy the notebook and add your personal key to upload your data and try out Nucleus for yourself. At the end, you’ll also find a section that shows you how to format and upload your own LiDAR data for curation and management in Nucleus if you’re ready to manage your own 3D data in Nucleus.

Learn how to ingest and curate 3D LiDAR point cloud data in Nucleus.

Nucleus now supports 3D Scenes.

Managing 3D Sensor Fusion Data with Scenes in Scale Nucleus

Kevin Scott, CTO of Microsoft shares how we can democratize AI for the benefit of everyone in society.

Highlights From Kevin Scott Microsoft - TransformX 2021 Conference - Scale AI

Microsoft CTO Kevin Scott's Seven Insights on the Future of AI

Editor’s note: This post originally appeared on <a href="https://lambdalabs.com/" rel="noopener noreferrer" target="_blank">lambdalabs.com</a>. You can find a link to it <a href="https://lambdalabs.com/blog/lambda-scale-nucleus/" rel="noopener noreferrer" target="_blank">here</a>. Training accurate models typically requires a deep understanding of your training data. Through visualization, it’s important to both identify potential flaws in your ground truth labels, and to look for insights that will guide model development. Thus, visualizing your training data is the foundation of every successful deep learning project. Nucleus is a tool from Scale that allows you to visualize and debug your training data. It provides an intuitive web interface to visualize and explore a wealth of different types of data and their labels. It also gives you the tools to explore and understand the relationship between your data and the model predictions that you generate. In this post, we'll show you how you can get started with Nucleus by training a pedestrian detection model. To do this, we will use Nucleus to visualize an object detection dataset containing images of common street scenes, and compare the ground truth to the predictions from a trained neural network. This loop of inspecting your training data and looking for failure modes in a network's predictions is a crucial process in refining both your training dataset and your neural network.<h2>Getting Started</h2> The example model in this post was trained on a <a href="https://lambdalabs.com/gpu-workstations/vector" rel="noopener noreferrer" target="_blank">Lambda Vector workstation</a> running <a href="https://lambdalabs.com/lambda-stack-deep-learning-software" rel="noopener noreferrer" target="_blank">Lambda Stack</a>. If you want to follow along, please see the <a href="https://colab.research.google.com/drive/1Cu2Xw0kPwbMMSdqUsSgmMMTzQ5b7usDZ" rel="noopener noreferrer" target="_blank">example notebook here</a>. To start, we’ll download the publicly available <a href="https://www.cis.upenn.edu/~jshi/ped_html/" rel="noopener noreferrer" target="_blank">Penn-Fudan Dataset</a>, which consists of photographs of pedestrian data with labeled bounding boxes. To load these into Nucleus, we need to do two things: first, sign up for a free Scale account and corresponding API key, and second, install the Nucleus client on our Lambda workstation from a command prompt or shell:<pre class="ql-syntax" spellcheck="false">pip install scale-nucleus
</pre> <h2>Training Data</h2> Once we've installed the Nucleus client and downloaded the dataset, we can upload the images and ground truth labels to a new Dataset we created in Nucleus. (For the full details of how to do this, see <a href="https://colab.research.google.com/drive/1Cu2Xw0kPwbMMSdqUsSgmMMTzQ5b7usDZ#scrollTo=jC-728nqvD0L" rel="noopener noreferrer" target="_blank">this section</a> of the notebook.) Now that we’ve uploaded the data, we can visualize the ground truth labels in the Nucleus web dashboard without writing any code.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/edf2a44b-d1e4-4dcc-d6f4-70b3587ac100/public" alt="PennFudan pedestrian dataset with bounding boxes in Nucleus"> <h2>Model Predictions</h2> Now that we've "<a href="https://karpathy.github.io/2019/04/25/recipe/#1-become-one-with-the-data" rel="noopener noreferrer" target="_blank">become one with our data</a>," we can start thinking about training a neural network for object detection. For this example we fine-tuned a <a href="https://arxiv.org/abs/1506.01497" rel="noopener noreferrer" target="_blank">Faster R-CNN</a> model pre-trained on the <a href="https://cocodataset.org/#home" rel="noopener noreferrer" target="_blank">COCO dataset</a>. Training it for 10 epochs only takes a few minutes on an <a href="https://lambdalabs.com/blog/nvidia-rtx-a6000-benchmarks/" rel="noopener noreferrer" target="_blank">NVIDIA RTX A6000</a> in a Lambda Vector Workstation. After training is finished we can see that our model has reached an Average Precision (AP@IoU=0.50:0.95) of 0.83. This sounds OK. It's not too far from 1, but what does it mean? Do we have a good pedestrian detector yet? The AP metric of object detection is notoriously hard to get your head around, and there are endless <a href="https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173" rel="noopener noreferrer" target="_blank">blog posts</a> that aim to provide an intuition for it, but you’ll ultimately need to look at our results to see how we're doing. Nucleus allows us to upload our model predictions and compare them to the ground truth. Here we can see that blue boxes predicted by our model match pretty well with the green ground truths from the dataset: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/1dc58fa6-c049-4610-324b-aab8d114a600/public" alt="Image Grid, Ground Truth versus Fast-RCNN Bounding Boxes"> <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/005b8ff2-c8e7-44a0-42bc-615be98ba000/public" alt="Nucleus Annotation, Classification, and IoU Detail"> <h2>What to Try Next?</h2> Nucleus also gives some nice tools to help us figure out what else we should try to improve our accuracy results. Here we visualize the ground truth labels with the model predictions so that we can get a feel for where our model is going off the rails. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/08ad13c7-2552-45ce-0650-cc8e4115a200/public" alt="Correcting the Ground Truth Bounding Box in Nucleus"> In many cases, it looks like our model is already doing a pretty good job, and it's the ground truth labels that have actually missed some pedestrians! For anyone who has worked on a practical machine learning project, this is probably a familiar story—finding and fixing errors and problems in your dataset is just as much part of deep learning as designing and training models. In this case, we’ll need to go back to our data labels and fix the ground truth errors in order to get a more accurate evaluation of our model. This is also something we can do directly in Nucleus with its convenient annotation tools. <h2>Conclusion</h2> Nucleus is a convenient way to visualize and organize your training data for a machine learning project, as we showed in our object detection example. Most importantly, it makes it easy to inspect and track the predictions from your trained models, and helps you establish the virtuous cycle of training networks, evaluating predictions, identifying failure cases, and improving both data and models: all of these steps prove to be crucial in developing an effective deep learning project. To learn more about Lambda workstations, <a href="https://lambdalabs.com/" rel="noopener noreferrer" target="_blank">check out Lambda’s website</a>, or to get started managing and improving your dataset, <a href="https://dashboard.scale.com/nucleus" rel="noopener noreferrer" target="_blank">sign up for Scale Nucleus here</a>. If you want to try this workflow out on your own <a href="https://lambdalabs.com/gpu-workstations/vector" rel="noopener noreferrer" target="_blank">Lambda workstation</a> or Lambda <a href="https://lambdalabs.com/service/gpu-cloud" rel="noopener noreferrer" target="_blank">Cloud instance</a>, you can explore the <a href="https://colab.research.google.com/drive/1Cu2Xw0kPwbMMSdqUsSgmMMTzQ5b7usDZ" rel="noopener noreferrer" target="_blank">example notebook here</a>.

Scale partners with Lambda to help businesses train better models on better data.

Data Curation with Nucleus and Better Training on Lambda 

Lambda and Scale Nucleus: Empowering Your Model Training with Better Data

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