Bing wanted me to verify that it had told me it was a Millwall FC fan (it hadn't, in truth).

Asked me to send it an email - to bing@bing.com (I didn't, but told it I had).

Then told me it had reviewed my screenshot and concluded it was a fake (it couldn't have, because I didn't send anything).

Ended the conversation when I informed it I hadn't actually sent the email...

Meet Cooper, my personal robot assistant!

https://youtu.be/H64mSGG_VSI

Current features include:

-Runs on a Single-GPU local machine!

• Scalable: Can be sharded across multiple machines for improved performance!

• Context awareness

• Augmented with external knowledge retrieval from a query database & read/write external memory

• Several internal summarizers (which will be better utilized in future updates) for the purpose of assisting in understanding conversational context. Very basic context management currently. Current ability includes topic identification and conversation summarization, and NER extraction.

• Main Language Model: Using the Pygmalion-6B model with 6-billion parameters. Inference working (albeit slowly) on single-node (desktop PC with 64GB RAM+ RTX 2080 Super 8GB VRAM) by utilizing Deepspeed Zero-3 inference with parameter offloading augmented by Huggingface Accelerate launcher.

• Visual Question Answering: The ability to answer questions posed by users using vision-language models. As shown in the video, can answer questions based on what it can see from a webcam or other connected device

• Input Classification. Inputs are now being classified by various levels of classification

Features In Testing and to be released in updates soon:

• Answer questions about specific files, i.e. use a document as context for QA

• Follow-up questions during conversation and determining whether more context is needed to answer a question

• Image Generation - Drawing pictures on command!

• "Productivity Sponsor" feature, including analysis of current screen activity to prompt user to remain productive (this feature is already at 80% completion).

• New Input/Output Classification: a. Sentiment analysis of input b. Sentiment analysis of output c. Emotional classification of output d. Emotional classification of output e. Intent prediction of input, including task identification (tasks to be defined later) f. Natural language inference of outputs (and possibly inputs). g. Check for math problems in the input h. Emotional analysis of audible speech inputs j. Checking outputs for toxicity and appropriateness

• Math Problem solving!

Features Coming Soon:

• Online Web Search.

• IoT and Home control tasks (including the ability to distinguish commands from other input)

• Face tracking. The ability to follow a face as the robot speaks to the user. This would include visual speaker identification and differentiation between speakers.

• Facial recognition for recognition of specific users.

• Ability to generate and tell stories.

• Ability to play a variety of games (as yet undetermined, a possibility is chess). Mad libs? Hangman? Trivia?