All You Need To Know About Software 2.0

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All You Need To Know About Software 2.0
Email ID- info_at_clarifai.com
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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.

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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 dont 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.  

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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 doesnt.

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The Software 2.0 way of doing things

• With Software 2.0, we dont 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. 

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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 models 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.

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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.

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Advantages of Software 2.0
  Content moderation AI models are being used
every day to filter out harmful image, video,
text and audio content from 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 inspection costs 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.
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Thank You!
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