Hear from top leaders discuss topics surrounding AL/ML technology, conversational AI, IVA, NLP, Edge, and more. Machine learning (ML) has broad applications -- and supervised ML, particularly, has taken off in recent years. Thus, it's critical that organizations build data engines that utilize the right data at the right stage of their projects' lifecycles, Manu Sharma told the audience at VentureBeat's Transform 2022 event. The founder and CEO of Labelbox explained that the "fundamental premise" of supervised ML is creating annotated or labeled data. This involves applying semantic annotations on any unstructured information, such as text and video.

Before you can even think about building an algorithm to read an X-ray or interpret a blood smear, the machine has to know what's what in an image. All of the promise of AI in healthcare -- an area that has attracted $11.3 billion in private investment in 2021, can't be realized without carefully labeled data sets that tell machines what exactly they're looking for. Creating those labeled data sets is becoming an industry itself, boasting companies well north of unicorn status. Today, Encord, a small startup just out of Y Combinator, is looking to take a piece of the action. Aiming to generate labeled data sets for computer vision projects, Encord launched its own beta version of an AI-assisted labeling program called CordVision.

Here's What You Need to Know: AI technology is fast evolving. The national security establishment is racing to adopt artificial intelligence in nearly every aspect of operations, from processing payroll to processing disparate battlefield information into a cohesive whole, such as in the Pentagon's Joint All Domain Command and Control effort to network otherwise separated operational "nodes" to one another in warfare to optimize and streamline attack. However, training AI systems to recognize the things they are meant to recognize requires vast, even seemingly limitless volumes of annotated data. As promising AI is, an AI system is only as effective as its training data. At the moment, there seem to be few barriers to AI and its promise for the future, yet an actual AI-system is only as effective as its database.

As I noted recently, organizations often find the biggest success through small steps with artificial intelligence. There are many examples of this at work, but Linux offers a great one. Linux started out as a student desktop experiment before it creeped slowly into companies as a reliable print server before eventually taking over the data center and the cloud (and Mars--it's on both the Chinese and U.S. rovers there). Incremental steps can add up to big things. In the area of food production, it needs to.

These days it's hard to find a public company that isn't talking up how artificial intelligence is transforming its business. From the obvious (Tesla using AI to improve auto-pilot performance) to the less obvious (Levis using AI to drive better product decisions), everyone wants in on AI. To get there, however, organizations are going to need to get a lot smarter about data. To even get close to serious AI you need supervised learning which, in turn, depends on labeled data. Raw data must be painstakingly labeled before it can be used to power supervised learning models.

SAN FRANCISCO, Feb. 04, 2020 (GLOBE NEWSWIRE) -- Labelbox, the leading training data platform for enterprise machine learning applications, today announced the close of a $25 million Series B funding round led by Andreessen Horowitz with General Partner Peter Levine joining the Labelbox board of directors. Previous investors First Round Capital, Gradient Ventures (Google's AI-focused venture fund) and Kleiner Perkins also participated. To date, Labelbox has raised $39 million in venture funding. Labelbox offers a training data platform for machine learning teams to build real-world artificial intelligence. The platform consists of label editor tools, batch & real-time labeling workflows, collaboration, quality review, analytics, and an optional, fully managed and dedicated labeling workforce.

I participated in an amazing AI challenge through Omdena's community where we built a classification model for trees to prevent fires and save lives using satellite imagery. Omdena brings together AI enthusiasts from around the world to address real-world challenges through AI models. My primary responsibility was to manage the labeling task team. Afterward, I had the chance to take on another responsibility and build an AI model that delivered results beyond expectations. I am Leo from Rio de Janeiro, Brazil and I m a mechanical aeronautics engineer who currently works as a data scientist and management consultant in Brazil helping several companies to achieve better business results.

After performing a TFRecord export, Labelbox provides us with a link to an export.json There are two keys in this file: tfrecord_paths contains Google Cloud Storage URIs to the exported TFRecords (the full list is truncated for brevity) and legends contains a mapping between class labels and the pixel value representing that class within the exported segmentation maps. Leveraging the tf.data.TFRecordDataset API and Google Cloud Storage (GCS)filesystem support, we can quickly and succinctly specify a dataset input pipeline while uses the TFRecord exports Labelbox has stored on GCS: The _parse_tfrecord function uses the schema documented here to deserialize tf.train.Examples from our TFRecords and decode the images into [width, height, 3] (three channels because these images have RGB colorspace) and labels into [width, height, 1] Tensors of float32s. The _resize function returns a function that uses bilinear interpolation to resize images and labels to have width and height both equal to image_dim (512 in our pipeline). An iterator over training_dataset can then be used as an input node into a TensorFlow graph, allowing us to train our model directly on the data collected on Labelbox.

Every artificial intelligence startup or corporate R&D lab has to reinvent the wheel when it comes to how humans annotate training data to teach algorithms what to look for. Whether it's doctors assessing the size of cancer from a scan or drivers circling street signs in self-driving car footage, all this labeling has to happen somewhere. Often that means wasting six months and as much as a million dollars just developing a training data system. With nearly every type of business racing to adopt AI, that spend in cash and time adds up. Labelbox builds artificial intelligence training data labeling software so nobody else has to.