Athena 

|  APPLY

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Use clevrML's groundbreaking Athena model for any image, language or categorical classification task. Apply now to get early access.

One AI Model For Many Tasks.

Over the past few months, clevrML has iterated on its unique AI technology "Active Memory Learning" to create a ground-breaking AI model called Athena. Athena can be applied to any image or language classification task without any explicit training or fine-tuning. 

Simple To Use For Any Developer

Breakthrough AI models are often hard to integrate into an app or service due to the complexity. With Athena, you can get real-time predictions with only a few lines of code. This reduces development time and gives your team more time to experiment. 

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Athena Benefits 

Realtime Learning

Athena has a unique ability to understand image or text classification tasks it has never seen before. By showing a few examples, Athena can learn in real-time to make a prediction. This allows developers to have high flexibility when applying Athena to different services.  

No More Model Building

Sourcing large datasets and training AI models are time-consuming and expensive. Since Athena can learn a new task with only a few examples, developers can easily integrate their specific use case quickly with the default version of Athena. This saves developers massive amounts of time and money.

Out of the box Solution

Athena was built to understand a wide variety of image and text categories. With this, you can use Athena's prior knowledge against your text or image inputs without providing any examples. 

Try Athena

Select a task and experience Athena for yourself.

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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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Apply Now For Early Access

If you are someone who would like to try Athena and be an early adopter, please fill out the application below. Athena will be a controlled rollout at first, with a public beta to follow. If you apply, you will be notified in the coming weeks if your application was accepted for early access.

Everyone is welcome to apply and there are no commitments to minimum usage.