All You Need To Know About Software 2.0

1. All You Need To Know About Software 2.0 Email ID- info@clarifai.com

2. About Clarifai • Founded in 2013 by Matthew Zeiler, a foremost expert in machine learning, Clarifai has been a market leader since winning the top five places in image classification at the ImageNet 2013 competition. • Recognized by leading industry analysts for our award-winning platform, Clarifai offers an end-to-end solution for modeling unstructured data for the entire AI lifecycle. Our powerful image, video, and text recognition solutions are built on the most advanced machine learning platform and made easily accessible via API, device SDK, and on-premise, empowering businesses all over the world to build a new generation of intelligent applications.

3. Software ate the world, and now AI – aka Software 2.0 – is eating software • A lot of our code is already in the process of being transitioned from Software 1.0 (code written by humans) to Software 2.0 (code written by AI, typically in the form of deep learning). • The models that drive AI don’t require the sort of predetermined structure of traditional software. In fact, one of the things that they are best known for is transforming disorganized and unstructured data into structured data. • Software 2.0 is very effective at working with data sources like images, video, text and audio, and just about all important advancements in these areas have been possible due to Software 2.0 in recent years. Click here to read the full blog

4. The Software 1.0 way of doing things • Programmers have traditionally built their systems by carefully and painstakingly instructing systems exactly what to do. • The world has built a huge amount of sophisticated tools that assist humans in dealing with the many unique challenges and opportunities that exist when writing code. • The programming process is slow, tedious and error-prone; anyone who has written computer code knows the experience of sitting in front of a computer screen for days staring at a program that should work, but doesn’t. Click here to read the full blog

5. The Software 2.0 way of doing things • With Software 2.0, we don’t really write code anymore. Instead, we program by example. • Programs are generated by analyzing large amounts of data, identifying patterns in this data and creating models of this data based on these patterns. • We collect many examples of what we want the program to do and what not to do, label them appropriately and train a model to interpret new inputs based on this information. • They not only use it to teach and train the model, but must be able to measure model performance, explainability and model drift.  Click here to read the full blog

6. Human in the loop: Labeling training data and iterative model development • In the new paradigm of Software 2.0, much of the attention of a software developer shifts from designing an explicit programming algorithm to designing and curating large datasets. • However, these systems are only as good as the training data they are learning from. • In many cases, machine learning systems are limited by human-caused flaws in the training data. • Improving a model’s performance frequently involves implementing a solid deployment environment, as well as maintaining a high quality stream of training data. • Developers need a monitoring system to ensure that the code which is written actually works. • We know that deep learning neural networks do well in supervised learning settings, and by supervised we (mostly) mean supervised by people. • If human beings can provide training data with both good and bad examples – or at least review and edit the ones generated by machines – these models can learn the patterns and provide correct outputs. Click here to read the full blog

7. The emergence of new roles in Software 2.0 • Software development has had job roles such as business analyst, systems analyst, architect, developer, tester and development-operations (DevOps). • These roles reflect the scoping, design, development, operations and maintenance phases of the software development lifecycle. • These roles are a hybrid of software engineering, software operations, statistics, machine learning and data management. • The existing body of engineering talent must start looking at the world differently, and there is a familiar set of resource problems faced in the shift to Software 2.0. Click here to read the full blog

8. Advantages of Software 2.0 Content moderation AI models are being used every day to filter out harmful image, video, text and audio contentfrom user-generated content streams. Advertisers are able to find off-brand or poor quality content, profanity and toxic speech in text posts and even inappropriate text in images can be detected and moderated. Facial recognition A wide range of use cases are based on identifying faces, comparing faces, searching faces and verifying identity based on faces. Facial recognition technology is being used to provide secure access to schools, airports and offices.  Predictive maintenance Airlines, manufactures and agricultural businesses are using computer vision technology to save maintenance and inspectioncosts and increase the lifespan of capital assets. Equipment monitoring, maintenance scheduling, asset planning and asset efficiency are all well-positioned to see significant benefits from Software 2.0. Click here to read the full blog

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