Towards Better AI

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Apply clevrML's cutting-edge Athena model to any image, text or categorical classification task for your app or service.

Use the world's first AI platform for building and using AI without writing code.

Image by Oskars Sylwan

Our Goal

Research AI Methods That Pursue Artificial General Intelligence for the Next Generation of Developers.

AI is moving towards a transformative point where algorithms will pivot from narrow to general-purpose in nature, and eventually, to Artificial General Intelligence. clevrML is researching the next generation of AI methods that will be general-purpose and is in pursuit of Artificial General Intelligence. We believe these cutting-edge developments should be available to as many people as possible, so we have created a Public API for the new technologies that come out of our research. When you use our APIs, you are supporting the development of more cutting edge research.

The First of Many

A Revolutionary Technology: Active Memory Learning.

clevrML has engineered a breakthrough AI method that allows developers to build state-of-the-art AI models with as little as 3 data points and no model training. We call it "Active Memory Learning" or AML for short. AML models remember exactly what you showed it and applies that knowledge to make predictions.  An amazing thing about AML is one architecture has been adaptable to Image, Text and Numeric data, and we are still researching what else it can be used for. 

Image by Oskars Sylwan

Demo

Try our cutting-edge APIs.

arrow&v

from clevrml import Athena

import os

key = os.environ['API-KEY']

model = Athena()

model.predict(

   api_key=key,

   learning_object={

       "text": [

            ["How are you?", "Greeting"],

            ["Hello!", "Greeting"],

            ["Good afternoon!", "Greeting"],

            ["Goodbye!", "Farewell"],

            ["See ya", "Farewell"],

            ["Talk to you later.", "Farewell"]

      ]

   },

   inference={"text": "Hi there"},

   use_prebuilt=False

)

from clevrml import Athena 

import os

key = os.environ['API-KEY']

model = Athena()

model.predict(

   api_key=key,

   inference={"text": "This was a great experience!"},

   use_prebuilt=True

)

from clevrml import Athena

import os

key = os.environ['API-KEY']

model = Athena()

model.predict(

   api_key=key,

   learning_object={

       "image": [

            ["dog1.jpg", "Dog"],

            ["dog2.jpg", "Dog"],

            ["cat1.jpg", "Cat"],

            ["cat2.jpg", "Cat"],

      ],

       "text": [

            ["Great service", "good-review"],

            ["Fantastic people and food", "good-review"],

            ["Never coming back", "bad-review"],

            ["Service was slow!", "bad-review"]

       ],

   },

   inference={

           "text": "This was a great night",

           "image": Poodle.jpg"

    },

   use_prebuilt=False

)

Output

Athena Prediction:

Greeting

Reply: Hello! :)

Output

Athena  Prediction: 

Postitive

Output

Athena Prediction

Image: Dog

Text: good-review

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