#generativeaiapplications

Concerning Bezoku

A South Florida, USA-based AI start-up called Bezoku collaborates with local communities to create generative AI applications. consumer-focused linguists, mathematicians, machine learning experts, and software engineers make up the Bezoku development team. They provide commercial, no-code consumer interfaces, enabling communities to take charge of generative AI for the first time. The goal of it is to provide a human-level language system that is safe, dependable, and reasonably priced. This method, which is often the result of a personal connection to a place, links groups via a common cultural lexicon.

The Challenge

Come explore Bezoku, the state-of-the-art generative AI that understands social language. The goal of this advanced Natural Language Processing (NLP) system is to link people, not just technology.

Everyone’s access to private and secure language technologies

Bezoku develops generative AI in collaboration with communities. For the first time, communities will be in charge of generative AI thanks to Bezoku‘s team of consumer-focused linguists, mathematicians, machine learning, and software engineers who create commercial no-code user interfaces. It has the following cutting-edge AI capabilities:

Human-centered design includes safety elements that promote cognitive liberty and shield communities from discrimination based on dialect.

complete privacy data encryption, offering a first-of-its-kind generative AI system using sophisticated mathematical techniques.

It gives populations speaking “low resource languages” priority. The library will swiftly expand to include other Hispanic languages, such as Cuban and Honduran, in addition to Haitian Creole. This will enable bilingual speakers to engage in a process known as code switching.

Compared to rivals with trillion parameters, Bezoku requires 5,300 times less computing per model. Bezoku significantly reduces pollution, thus AI has less of an adverse effect on the planet’s limited resources.

The Solution

Large Language Models (LLMs) are true superpowers in terms of producing accurate and timely replies. but lack the in-depth, domain-specific expertise that, in certain use cases, is crucial. Not to mention the significant computational power and knowledge needed to create and implement them.

This is the situation where Small Language Models (SLMs) are increasingly being used for certain purposes. SLMs have a lower computational footprint and are more efficient than LLMs due to their more concentrated approach. They are thus better suited for local execution on workstations or on-premise servers.

The Resolution

It is a unique kind of artificial intelligence (AI) technology that is centered on people and has many security safeguards to guard against discrimination based on language and cognitive liberty. End-to-end encryption of private data is offering a first-of-its-kind solution for generative AI using sophisticated mathematics in order to do this.

For communities who speak “low resource languages,” it was created. They are beginning with Haitian Creole but will swiftly move on to many other Hispanic dialects and will support a bilingual speaker’s ability to switch between languages known as code switching. The Small Language Model (SML) method used by Bezoku requires 5,300 times less computing power per model than ChatGPT4. As a result, Bezoku emits significantly less pollution and has a much smaller environmental effect.

Bezoku core features are as follows: 

Image credit to Intel

Safe Data Handling: Bezoku uses encryption to protect sensitive data, making sure that only authorized users may access it.

Customized linguistic Understanding: To guard against prejudice stemming from dialect bias, the pre-trained models are adapted to various dialects and linguistic styles.

Simple Adaptation: Bezoku‘s training using private data doesn’t need any code. Easy-to-use, user-friendly tools load personal data, and the models automatically pick it up. That is all.

Transparent Predictions: Bezoku builds trust by not just offering solutions but also explaining how it works, for as by letting you know if it is experiencing hallucinations.

Bezuko’s strategy is comprised on four primary differentiators:

Adding dialects will increase representation and allow for the participation of other populations.

Affordability at a small fraction of LLM expenses using one’s own models.

Encrypted security data that is private by design.

Utilizing 5,000 times less energy and water each model is better for the environment.

“Bezoku is pleased to announce that it has joined the Intel Liftoff Program. Being a member of this exclusive worldwide network of technological companies, the Bezoku team and its partners are eager to use the array of potent AI tools and Developer Cloud to expedite the development of our products.

This partnership not only helps us expand and become more visible, but it also creates special relationships inside the IT industry. They can’t wait to start this adventure, to make use of Intel’s vast resources, to get access to their knowledgeable insights, and to work with other AI-focused firms. Ian Gilmour, Bezoku’s Chief Customer Officer

In summary

Bezoku’s groundbreaking human-centric approach is made possible by its generative AI technology. Smaller AI models as a consequence preserve cognitive liberty, eliminate dialect bias, and provide understandable supporting output by tracking data and alerting users if hallucinations are suspected.

As a result, groups with “low resource languages” end up receiving priority. This includes Hispanic dialects and Haitian Creole, among other things, since they will support bilingual speakers’ language

Extended reality Technology

It is not new to envision the future of education and the art of learning in general. The French artist Jean-Marc Côté imagined what education would look like in 2000, more than a century ago. The notion that literature might be electronically uploaded and processed straight into young people’s brains made many laugh when they saw his picture, but the idea would resurface in the science fiction film “The Matrix” from 1999.

Can art imitate life, though? In the upcoming years, will extended reality (XR) and artificial intelligence (AI) merge to create learning environments akin to those portrayed by Côté in “The Matrix”?

Just 16 billion, or 2%, of the $6.5 trillion K–12 education market is presently allocated to educational technology. Even so, a lot of teachers use technology to help their students reach their full potential. Even so, the majority of the time technology is only utilised to enhance passive teaching techniques (such as instruction via flat interactive panels, digital textbooks, online videos, and testing), which leads to minimal to nonexistent increase in academic achievements because of low student engagement. Active teaching techniques provided by AI and extended reality learning experiences, on the other hand, might be more beneficial to pupils.

The majority of extended reality experiences offered by content providers now are around STEM-based simulations, digital twins, virtual tours, and games. The convergence of AI modalities, however, presents us with a really transformative potential to fulfil the promise of “personalised” learning and produce quantifiable academic outcomes. These different generative AI applications will be put to use in the near future, automating them to provide real-time learning opportunities.

What can be done in the classroom today with generative AI?

With the help of generative AI, educators may design highly personalised classes. With minimal technical expertise, educators can rapidly produce dynamic movies to use as teaching aids through a series of manual procedures. Let’s discuss one such avenue for producing this kind of content:

Write a basic screenplay:

Request that Bard or ChatGPT compose a brief three- to five-minute video script that addresses the problem or instructive subject you want to tackle. Modify any content to meet quantifiable learning objectives.

Change to a script for a conversation:

To make the information produced by AI seem more real and conversational, copy and paste the original text into a programme like Quillbot.

Put together your audio story:

Paste the updated conversational text into Speechify or another application. You can choose from a variety of voices, such as Snoop Dogg and Gwenyth Paltrow, if you have their premium membership.

Make a video:

Using programmes like Movio or Invideo, upload your script and audio file, select the genre, and choose the delivery method (computer or the smartphone).

With the help of this technique and numerous others, teachers can add even more personalisation to their customised lectures. For instance, generative AI can produce lesson plans that highlight a student’s strengths and target their areas for growth. Teachers can free up time by training large language models to engage in discussions with children for targeted, on-demand support.

Though education has undoubtedly advanced in the current era thanks to technology, we primarily witness this in the form of computer and the tablet screens. Even with the familiar form of today’s 2D technology, children still find up staring at screens rather than engaging with their teachers and fellow students. Here’s where extended reality enters the picture and aims to close this gap. Combining generative AI with spatial computing capabilities to create an immersive learning environment that engages students with interactive and collaborative courses is another way that generative AI can be used in education.

Utilising artificial intelligence in extended reality learning

These days, the majority of extended reality learning opportunities take the form of walkthroughs or simulations, which are essentially an upgrade over standard lab and video materials. Most of them are singular experiences with distinct beginnings and ends. According to a PwC report, users were almost three times more confident in their ability to apply the skills they had gained from their extended reality training, and even with extended reality learning as it currently exists, learners absorbed information four times faster than in a typical classroom.

What if a real-time platform was equipped with the greatest generative AI techniques? I think the end product is a transformative tool that enables each learner to reach their full potential. Let’s examine a few ways that this idea of (Ai)daptive extended reality pronounced Ai-daptive can produce significant outcomes:

Introduces learning at the student’s present level:

Using (Ai)daptive XR to take into account the student’s actual abilities. Content is presented in that language right away if they feel more at ease doing so (thereby getting around a common challenge faced by ELL students nowadays).

Personalisation:

(Ai)daptive XR can provide actual personalisation, yet current attempts at personalisation frequently amount to nothing more than differentiated learning. The programme can quickly identify strengths and weaknesses and modify the learning objectives based on the first few difficulties in a simulation. For instance, if the learner finds it difficult to perform complex multiplication and the simulation’s objective is to measure the volume of spaces, the student will usually fail the simulation with minimal progress.

(Ai)daptive XR may identify the gap’s underlying cause fast and modify the learning objectives to assist the child in completing the prerequisites before reaching the original learning aim. On the basis of the identified learning obstacles, a new extended reality simulation is developed. Instead of letting these weaknesses build up and lead to the child’s future academic failure, the learner can then be better equipped to successfully achieve the objectives on a subsequent simulation.

Making learning relevant:

When students don’t recognise the relevance of the material, most classroom subjects fail to “engage” them. However, (Ai)daptive extended reality content can be swiftly modified to offer the material in a theme that is both effective and meaningful to the student by taking into account the student’s personal passion. For a young child who dreams of becoming a fashion designer, for instance, a simulation measuring an area of farmland may be instantly updated in real-time to measure an area of cloth. The result of that simulation may alter significantly as a result of that swift adjustment.

User-responsive:

(Ai)daptive XR is capable of responding to voice input from the user(s) in their native language, instead of just judging performance based on where the user “clicks” with a hand controller. Though conversational speech from the user will allow for more realistic interaction and enable the system to address inquiries or concerns, current extended reality simulations are restricted in their ability to gather user response.

Real-time feedback:

Learners can reinforce their mastery of earlier topics through simulations, in addition to seeing the actual ramifications of their decisions. Teachers frequently don’t have enough time in typical classroom settings to administer evaluations to their students. However, real-time natural assessments can be carried out in a stress-free setting several times a day with extended reality.

Collaboration:

Since it is simpler to produce individual-based content, few extended reality learning simulations permit numerous students to collaborate on a task. When a group of students collaborates, they can offer difficulties and novel scenarios, and (Ai)daptive XR can evaluate these and modify the content presented based on the group’s behaviour in the simulation.

Benefits of Extended Reality

With (Ai)daptive extended reality, the possibilities are virtually limitless

In the upcoming ten years, the development of instructional extended reality with AI will be a fantastic and welcome addition to EdTech. It is an opportunity to provide every child in every community with truly customised training. Considerable debates in a number of areas, such as governance, privacy, connectivity, and equity, will be necessary in light of these changes.

AI will permeate every aspect of our everyday lives as examples of generative Artificial Intelligence continue to be adopted by the general public over the course of the next ten years. Qualcomm Technologies, with its expertise in extended reality processing and AI, is at a crossroads in this industry. Seeing this through to completion throughout my lifetime is my dream.

Utilise Amazon Bedrock with AWS Step Functions to create generative AI applications

AWS are pleased to announce today two newly improved integrations between Amazon Bedrock and AWS Step Functions. Step Functions is a visual workflow tool that aids in the creation of data and machine learning (ML) pipelines, distributed application development, process automation, and micro service orchestration.

AWS released Amazon Bedrock, the simplest method for creating and expanding generative AI applications using foundation models (FMs), in September.

A wide range of features required by clients to develop generative AI applications while upholding security and privacy are provided by Bedrock, including a selection of foundation models from top suppliers such as Anthropic, Cohere, Stability AI, and Amazon.

Amazon Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs are the ways in which you may use Amazon Bedrock.

With Amazon Bedrock, you can create generative AI applications by coordinating activities and integrating with more than 220 AWS services thanks to the recently released Step Functions optimized connectors. You can create, review, and audit your processes graphically using Step Functions. To utilize Amazon Bedrock formerly required calling an AWS Lambda function from your workflows, which added code to maintain and raised the application’s cost.

Two newly optimized API operations for Amazon Bedrock are offered by Step Functions:

InvokeModel: With the help of this integration, you may call a model and use the parameters’ input to perform inferences. Run embedding, text, and picture model inferences using this API activity.

This integration generates a fine-tuning task for customizing a basic model, called Create Model Customization Job. The foundation model and the training data location are specified in the parameters. Once the task is finished, your personalized model is prepared for use.

This integration enables Step Functions to execute an asynchronous API call and wait for its completion before moving on to the next stage. This indicates that the state machine execution will stop to allow the generate model customisation operation to finish, then it will automatically restart.

Requests and answers may be up to 25 MB in size when using the InvokeModel API action. Nevertheless, the maximum state payload input and output for Step Functions is 256 kB. With this integration, you may provide an Amazon Simple Storage Service (Amazon S3) bucket where the InvokeModel API receives data and stores the result in order to handle bigger payloads. The API action configuration parameters section has a parameter section where these setups may be supplied.

How to use AWS Step Functions with Amazon Bedrock to get started

Make sure you establish the state machine in a region where Amazon Bedrock is accessible before you begin. Use US East in this example.

Establish a new state machine using the AWS Management Console. The two possible API actions will show up when you search for “bedrock.” The InvokeModel may be dragged to the state machine.

The menu on the right now allows you to customize that condition. You may choose the base model you want to utilize first. Either choose a model from the list or dynamically generate the model based on the input.

Next, you must set up the model’s parameters. You have the option to import the parameters from Amazon S3 or input the inference parameters in the text field.

You may select further configuration parameters for the API, such the S3 destination bucket, if you continue scrolling through the API action settings. If this field is filled in, the API action saves the answer to the API in the designated bucket rather than sending it back to the state output. Additionally, you may choose the kind of material for both requests and answers here.

Once your state machine configuration is complete, you may build and execute it. You may choose the Amazon Bedrock state, see the execution details, and examine the inputs and outputs of the state machine as it is operating.

Step Functions allow you to construct state machines as complex as required, integrating various services to address a wide range of issues. For instance, you may construct apps with prompt chaining by combining Step Functions with Amazon Bedrock.

By giving the FM many shorter, more manageable suggestions rather than one lengthy, intricate one, this strategy allows developers to create sophisticated generative AI systems. You may construct a state machine that makes repeated calls to Amazon Bedrock to get an inference for each of the smaller prompts, therefore constructing a prompt chain. This can all be done in parallel using the parallel state. Then, using an AWS Lambda function, you can combine all of the parallel task answers into a single response and provide a result.