BlogCategoryTag

Header

Footer

PreFooter

BlogPage

Trusted by world class companies, Scale delivers high quality training data for AI applications such as self-driving cars, mapping, AR/VR, robotics, and more.

AI & ML Blog | Scale AI

PaginationButton

AnnouncementContextProvider

UnnamedGlobalGroupOfVariants2

Screen

Segment

Experiment

Experiment2

scale.com (Production)

plasmic-nav

plasmic-embed-css

plasmic-basic-components

plasmic-tabs

plasmic-cms

react-chartjs-2

react-slick

react-quill

Product

Company

General

People

Engineering

Earlier this week, Meta announced the release of <a href="https://ai.meta.com/llama/" rel="noopener noreferrer" target="_blank">Llama 2</a>. Llama 2 includes both a base pre-trained model and a fine-tuned model for chat available in three sizes. While the performance of the pre-trained model is impressive, fine-tuning the base Llama-2 model can unlock even greater performance on most language tasks. This process can be quite complicated, from reserving the right compute resources to properly implementing the training and inference code. Scale's new open-source <a href="https://github.com/scaleapi/llm-engine" rel="noopener noreferrer" target="_blank">LLM Engine</a> repository makes this process easy and accessible. Let's dive into how you can use this API and explore some examples.<pre class="ql-syntax" spellcheck="false">from llmengine import FineTune

response = FineTune.create(
 model="llama-2-7b",
 training_file="s3://my-bucket/path/to/training-file.csv",
)

print(response.json())
</pre>You can also follow the code examples in this <a href="https://github.com/scaleapi/llm-engine/blob/main/examples/finetune_llama_2_on_science_qa.ipynb" rel="noopener noreferrer" target="_blank">notebook</a>. <h1>Dataset</h1>For this example, we will use <a href="https://scienceqa.github.io/" rel="noopener noreferrer" target="_blank">ScienceQA</a>, a popular dataset consisting of a diverse set of multiple-choice science questions. Each question may have textual context and image context and contains a thorough explanation and lecture supporting the solution. <img src="https://lh3.googleusercontent.com/hB2u6hfFZMXlmtwOLgT3-wrJKdlyAdd9MssPkfvoysG2KRdnafhfxHfJ4IVRKDt1Bj71LikV-vtmXBK1P11sOqS8ea7nGnD1_1mVIKAIcQn4OIbKNKESnpSobwRFBsnTAbYT_7t_RARN83xNiwGRitk" height="260" width="624">An example sample from ScienceQA Currently, LLM Engine supports fine-tuning on prompt-completion pairs. Let's first convert this dataset into the supported format, a CSV with two columns: <code>prompt</code> and <code>response</code>. Before you get started, install the required dependencies. <code>pip install datasets==2.13.1 smart_open[s3]==5.2.1 pandas==1.4.4</code> We can load the dataset from Hugging Face, and observe the dataset's features. <pre class="ql-syntax" spellcheck="false">from datasets import load_dataset
from smart_open import smart_open
import pandas as pd
dataset = load_dataset('derek-thomas/ScienceQA')
dataset['train'].features
</pre>A commonly used format for feeding ScienceQA examples is:<pre class="ql-syntax" spellcheck="false">Context: A baby wants to know what is inside of a cabinet. Her hand applies a force to the door, and the door opens.
Question: Which type of force from the baby's hand opens the cabinet door?
Options: (A) pull (B) push
Answer: A.
</pre>Since the format of <code>options</code> in the Huggingface dataset is only a list of possible answers, we need to convert this list into the example format from above by adding the enumeration prefix. <pre class="ql-syntax" spellcheck="false">choice_prefixes = [chr(ord('A') + i) for i in range(26)] # A-Z
def format_options(options, choice_prefixes):
 return ' '.join([f'({c}) {o}' for c, o in zip(choice_prefixes, options)])
</pre>&nbsp;Now, let's write the formatting function to turn a single sample from this dataset into a <code>prompt</code>&nbsp;and <code>response</code> to feed into the model.<pre class="ql-syntax" spellcheck="false"> def format_prompt(r, choice_prefixes):
 options = format_options(r['choices'], choice_prefixes)
 return f'''Context: {r["hint"]}\nQuestion:{r["question"]}\nOptions:{options}\nAnswer:'''
def format_response(r, choice_prefixes):
 return choice_prefixes[r['answer']]
</pre>Finally, let's construct the dataset. Note that some samples in ScienceQA only have an image for context - we'll be skipping those in this example as Llama-2 is purely a language model, and cannot accept image inputs. <pre class="ql-syntax" spellcheck="false"> def convert_dataset(ds):
 prompts = [format_prompt(i, choice_prefixes) for i in ds if i['hint'] != '']
 labels = [format_response(i, choice_prefixes) for i in ds if i['hint'] != '']
 df = pd.DataFrame.from_dict({'prompt': prompts, 'response': labels})
 return df
</pre>LLM Engine supports training with a training and validation dataset. If you only provide a training dataset, the LLM Engine will randomly split 10% from the dataset for validation. Splitting your dataset prevents the model from overfitting on your training data, resulting in poor generalization to live data it might see during inference. These dataset files must be stored in a publicly accessible URL so that LLM Engine can read them. For this example, we will be saving the datasets to s3. We also have the preprocessed <a href="https://gist.githubusercontent.com/jihan-yin/43f19a86d35bf22fa3551d2806e478ec/raw/91416c09f09d3fca974f81d1f766dd4cadb29789/scienceqa_train.csv" rel="noopener noreferrer" target="_blank">train dataset</a> and <a href="https://gist.githubusercontent.com/jihan-yin/43f19a86d35bf22fa3551d2806e478ec/raw/91416c09f09d3fca974f81d1f766dd4cadb29789/scienceqa_val.csv" rel="noopener noreferrer" target="_blank">validation dataset</a> publicly&nbsp;available in Github Gists - you can directly replace <code>train_url</code> and <code>val_url</code> with these links. <pre class="ql-syntax" spellcheck="false">train_url = 's3://...'
val_url = 's3://...'
df_train = convert_dataset(dataset['train'])
with smart_open(train_url, 'wb') as f:
 df_train.to_csv(f)
df_val = convert_dataset(dataset['validation'])
with smart_open(val_url, 'wb') as f:
 df_val.to_csv(f)
</pre>Now, we can start fine-tuning via the LLM Engine API. <h1>Fine-Tuning</h1>First, we need to install LLM Engine. <code>pip install scale-llm-engine</code> Next, you'll need to set up your Scale API key. Follow the instructions in the&nbsp;<a href="https://github.com/scaleapi/llm-engine#-quick-start" rel="noopener noreferrer" target="_blank">README</a> to acquire your unique API key. Advanced users can also follow the guide for <a href="https://scaleapi.github.io/llm-engine/guides/self_hosting/" rel="noopener noreferrer" target="_blank">self-hosting LLM Engine</a> to avoid needing a Scale API key. <pre class="ql-syntax" spellcheck="false">import os
os.environ['SCALE_API_KEY'] = 'xxx'
</pre>Once you have everything set up, fine-tuning your model requires only one API call. Today, we'll be working with the 7 billion parameter version of Llama-2, which proves powerful enough for most use cases. <pre class="ql-syntax" spellcheck="false">from llmengine import FineTune
response = FineTune.create(
 model="llama-2-7b",
 training_file=train_url,
 validation_file=val_url,
 hyperparameters={
 'lr':2e-4,
 },
 suffix='science-qa-llama'
)
run_id = response.fine_tune_id
</pre>With the <code>run_id</code>, you can monitor the status of your job and get live updates on per-epoch metrics, like train and validation loss. ScienceQA is a large dataset, so it may take an hour or two for training to complete. <pre class="ql-syntax" spellcheck="false">while True:
 job_status = FineTune.get(run_id).status
 # Returns one of `PENDING`, `STARTED`, `SUCCESS`, `RUNNING`,
 # `FAILURE`, `CANCELLED`, `UNDEFINED` or `TIMEOUT`
 print(job_status)
 if job_status == 'SUCCESS':
 break
 time.sleep(60)
#Logs for completed or running jobs can be fetched with
logs = FineTune.get_events(run_id)
</pre><h1>Inference &amp; Evaluation</h1>Once you are done fine-tuning, you can start generating responses to any input. However, before that, let's ensure the model exists and is ready to accept inputs. <code>ft_model = FineTune.get(run_id).fine_tuned_model</code> It may take a few minutes for your first inference call to be served. After that, inference should be relatively quick. Now, let's evaluate the performance of our Llama-2 model fine-tuned on ScienceQA.<pre class="ql-syntax" spellcheck="false">import pandas as pd

#Helper a function to get outputs for fine-tuned model with retries
def get_output(prompt: str, num_retry: int = 5):
 for _ in range(num_retry):
 try:
 response = Completion.create(
 model=ft_model, 
 prompt=prompt, 
 max_new_tokens=1, 
 temperature=0.01
 )
 return response.output.text.strip()
 except Exception as e:
 print(e)
 return ""
#Read the test data
test = pd.read_csv(val_url)
test["prediction"] = test["prompt"].apply(get_output)
print(f"Accuracy: {(test['response'] == test['prediction']).mean() * 100:.2f}%")
</pre> This achieves 82.15% accuracy. This is pretty good! Let's see how this compares with the original base Llama-2 model. Since the pre-trained model was not fine-tuned on these examples, we need to provide an example in the prompt so the model learns to adhere to the format we expect from the responses. We also see how things compare against fine-tuning a model of similar size, Mosaic's&nbsp;<a href="https://www.mosaicml.com/blog/mpt-7b" rel="noopener noreferrer" target="_blank">MPT</a>.<img src="https://static.scale.com/uploads/6019a18f03a4ae003acb1113/blog-llama-1.png">*Results from <a href="https://arxiv.org/abs/2302.00923" rel="noopener noreferrer" target="_blank">MM-COT paper.</a> As we can see, fine-tuning makes a huge difference in the case of both language models. The performance gain from fine-tuning Llama-2 on ScienceQA was a&nbsp;26.59%&nbsp;absolute difference! This is in addition to the fact that inference with a fine-tuned model is cheaper than few-shot example prompts, due to shorter prompt length. This fine-tuned Llama-2-7B model also outperforms GPT-3.5, a 175 billion parameter model! We can also see that the new Llama-2 model outperforms MPT in both the fine-tuned and few-shot prompting settings, showcasing its strength as both a base and fine-tunable model. As a side experiment, we also used LLM Engine to fine-tune and evaluate LLAMA-2's performance on several tasks from <a href="https://gluebenchmark.com/" rel="noopener noreferrer" target="_blank">GLUE</a>, a set of commonly used NLP benchmark datasets.<img src="https://static.scale.com/uploads/6019a18f03a4ae003acb1113/blog-llama-2.png"><h1>Conclusion</h1>Fine-tuning makes a huge difference in unlocking greater performance from base pre-trained models. Now you are all set to unleash the true potential of your fine-tuned model and witness the magic of powerful AI-generated responses. Happy fine-tuning! 🚀

image.jpg

Deploy and fine-tune models using our open-sourced LLM Engine Repo. Learn how in our latest blog. 

Fine-Tune Llama 2 with Scale's LLM Engine Repository

How to Fine-Tune Llama 2 With LLM Engine

Today, every company is investing in AI initiatives after the explosion of generative AI in the past six months. Those that don&rsquo;t will get left behind. Yet many companies don&rsquo;t know how to implement AI into their business operations. Customizing models is extremely difficult, requiring expertise in ML Infrastructure and experience with a large number of software packages. Often these software packages are cobbled together and don&rsquo;t achieve the desired results. Enterprises rarely have the internal expertise or time to ensure AI is done right.&nbsp;
So today, we&rsquo;re launching an <a href="https://scale.com/custom-llms">open-source platform</a> for customizing large language models (LLMs) for enterprise use cases. The platform allows enterprises to customize open-source LLMs to achieve optimal performance for their specific use cases using their own data. Businesses can more easily build applications for their highest-priority generative AI use cases. Fine-tuning open-source models with Scale will give organizations better performance for their specific use cases, increased customizability, and enhanced safety at a lower total cost than other solutions.
As part of our efforts, Scale is proud to be a <a href="https://ai.meta.com/llama/#partnerships" target="_blank" rel="noopener">Meta Llama 2 Launch Partner</a>, and we're excited to make it easier to customize open-source LLMs like Llama 2. Scale CEO and founder Alexandr Wang also signed a <a href="https://about.fb.com/news/2023/07/llama-2-statement-of-support/" target="_blank" rel="noopener">statement in support</a>&nbsp;of Meta&rsquo;s open approach to AI that enables everyone to benefit from the technology and brings additional levels of visibility, scrutiny, and trust.&nbsp;
<h2 dir="ltr">What Scale is Making Possible</h2>
With this new offering, enterprises can customize open-source models with just a few lines of code leveraging their own data. For more demanding model customization needs, Scale&rsquo;s fine-tuning platform, combined with our <a href="https://scale.com/data-engine">Data Engine</a>, will optimize model performance for the most challenging business use cases. The Scale Data Engine powers the most advanced LLMs and generative models through world-class RLHF, data generation, model evaluation, safety, and alignment, making it easy for every enterprise to adopt industry-leading generative AI.
Scale helps insurance companies summarize and classify claims more efficiently, retail and e-commerce companies improve the shopping experience through personalized recommendations, and wealth managers increase returns by quickly summarizing and identifying the most relevant trends from financial data. Achieving this performance at these specific tasks is not possible without highly capable base models that are fine-tuned with high-quality data.
Our new platform enables:
<ul>
<li dir="ltr" aria-level="1">
Performance: Models customized with proprietary data perform better at high-priority tasks.
</li>
<li dir="ltr" aria-level="1">
Customizability: Customize models to specific use cases, and shape how they respond to common scenarios so they can be an effective extension of the brand.
</li>
<li dir="ltr" aria-level="1">
Safety: Enhance the safety of models with human-in-the-loop testing, evaluation, and monitoring.
</li>
</ul>
<h2>How to Get Started</h2>
To support an open innovation approach to AI, Scale is providing an <a href="https://github.com/scaleapi/llm-engine">open-source package</a> and open APIs for model fine-tuning and hosting, along with clear documentation on how to use these APIs. The package and APIs empower any organization, no matter their expertise, to get up and running quickly and easily. Enterprises that need an additional performance boost for their specific use cases can leverage the power of human experts to enhance their data with the Scale Data Engine. Ultimately, this platform will deliver fine-tuned open-source models for organizations&rsquo; most important use cases and for specific domains to encourage the adoption of generative AI across industries.
Get started by downloading the&nbsp;<a href="https://github.com/scaleapi/llm-engine">open-source repo from Github,</a> or if you are looking to optimize your models with our enterprise model customization platform and the power of the Scale Data Engine, <a href="https://scale.com/custom-llms">contact us</a> today.

Scale is proud to be a Meta Llama 2 launch partner and we're excited to make it easier to customize open-source LLMs like Llama 2 with our platform for customizing open-source large language models (LLMs) for enterprise use cases.

Scale Unlocks Open-Source LLMs With New Platform and Partnership with Meta

<a href="https://scale.com/">Scale AI</a>, the data infrastructure for AI, has been selected by the U.S. Army and the <a href="https://www.diu.mil/">Defense Innovation Unit</a> (DIU) to deploy their Data Engine in support of the Army&rsquo;s Robotic Combat Vehicle (RCV) program. The effort is a joint collaboration between DIU and the Army&rsquo;s Program Executive Office Ground Combat Systems (PEO GCS) leveraging innovative Other Transaction Authority for the novel application of Scale&rsquo;s commercial technology to the RCV program.
The Army has determined that the next stage of navigating high-risk environments requires advanced technology, like trusted autonomous systems, to safely conduct reconnaissance and related tasks. Scale will play a vital role in the development of these systems by providing its Data Engine platform to support raw data collection, curation, and labeling, as well as AI model development and test and evaluation for autonomous navigation software and computer vision algorithms.&nbsp;&nbsp;&nbsp;
Mitigating risk for combat troops is paramount in military operations, and Scale is committed to supporting the U.S. Army&rsquo;s efforts to ensure the safety of our service men and women. With a strong background in the autonomy space, Scale has established partnerships with notable customers such as General Motors, Lyft, and Toyota. Leveraging their deep expertise, Scale will provide robust support for the RCV program, delivering proven commercial technology tailored for defense applications, including off-road terrain, extreme weather conditions, and contested or degraded communications.
Whether you&rsquo;re a fighter pilot in the Air Force, or an Armor Officer in the Army, nothing replaces real world training events when developing combat-ready Airmen or Soldiers; synthetics and simulation can only get you so far and AI for autonomy is no different. Scale is excited to partner with the U.S. Army who will benefit from our deep expertise in autonomy combined with our <a href="https://scale.com/data-engine">Data Engine </a>technology, to deliver the data management plans and platforms DoD autonomy programs need.&nbsp;
Come talk to us at the GEOINT conference in St. Louis, MO from 22-24 May. We will be at booth #633 or check us out on LinkedIn!
Learn more here: <a href="https://scale.com/data-engine">https://scale.com/data-engine</a>
&nbsp;

Scale AI, the data infrastructure for AI, has been selected by the U.S. Army and the Defense Innovation Unit (DIU) to deploy their Data Engine in support of the Army’s Robotic Combat Vehicle (RCV) program. 

Scale

Scale AI chosen by the U.S. Army for Robotic Combat Vehicle Program

<h2 dir="ltr">Background</h2>
Object tracking in aerial imagery is largely an unsolved problem in the AI overmatch future powered by Generative AI and platforms like <a href="https://scale.com/donovan" target="_blank" rel="noopener">Scale Donovan</a>; expert algorithms ATR and chain-of-custody play a crucial role as tools enabling the models to expand out from a pure text and structured data domain.
Although there exists extensive research on object tracking in the academic realm, those solutions are largely targeted at accurate assignment of track ID&rsquo;s on top of fixed object detector outputs. These outputs come from a nearly perfect detector on common commercial footage. This footage typically comes from a camera that is mounted front-facing on the ground in a fixed position with fixed zoom levels.&nbsp;
However, the needs of many Federal customers do not align with these cookie-cutter solutions. They often require aerial imagery and full motion video capabilities, which present additional challenges, on top of accurate assignment of track ID&rsquo;s as in the commercial realm:&nbsp;
<ol>
<li dir="ltr" aria-level="1">
The base object detectors are less performant, meaning there is significant room for the tracker to use temporal information to reduce false positives and false negatives.
</li>
<li dir="ltr" aria-level="1">
The camera moves quite a bit in terms of position (i.e., panning) and zoom levels.
</li>
<li dir="ltr" aria-level="1">
Long-term re-identification of targets is non-trivial and mission-critical.&nbsp;
</li>
</ol>
<h2 dir="ltr">Scale&rsquo;s Solution: A Suite of Tracker Post-Processors</h2>
To address these problems, Scale offers a suite of tracker post-processors, each of which can be turned on or off.&nbsp; We optionally package these on top of our own custom, base tracker which achieves state-of-the-art balance between precision and recall when tracking in the presence of camera movement.&nbsp;
These post-processors can be used alone to enhance an existing tracker, or used as an end-to-end tracking package on top of an existing detector. This package can be called in one line of code per frame after each object detector inference (for users using the package) or base tracker inference (for users using just the post-processors).&nbsp;
<h2 dir="ltr">Sample Demo MP4 Results</h2>
Below, we compare our Scale Tracker packaged with our custom base tracker (25FPS on the left) with two of the best publicly available trackers - one that is faster (BYTETrack at 30FPS in the middle) which has comparatively poor track ID permanence, and one that is slower with slightly better track ID permanence than BYTETrack (SmileTrack at 10FPS on the right), although still not as good as Scale Tracker&rsquo;s track ID performance:
<img title="image1.gif" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/01b73d81-ae6e-4a6d-b744-8cdcc98aedce" alt="" width="1200" height="300">
<img title="image2.gif" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/3945304f-e9e7-4aaf-a166-ff9f2ab89934" alt="" width="1200" height="300">
<img title="image3.gif" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/8dd6967b-f7bb-414d-aa5d-58952facd8d8" alt="" width="1200" height="300">
The Scale tracker:
<ol>
<li dir="ltr" aria-level="1">
Flickers less -- this creates more visually persistent tracks thereby creating a better user experience
</li>
<li dir="ltr" aria-level="1">
Is able to maintain track ID&rsquo;s through periods of erroneous track dropping -- this creates tracks that exhibit more permanence
</li>
</ol>
<h2 dir="ltr">Empirical Results</h2>
<h3 dir="ltr">Academic results</h3>
<img title="image4.png" src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/0691ca39-f375-405a-8081-c8cfaf0484ad" alt="" width="615" height="438">
Note: The metrics for the other methods in the above chart were taken directly from the <a href="https://arxiv.org/pdf/2202.13514.pdf">StrongSORT paper</a>
Even though our tracker is aimed at Federal problems, it actually improves upon the performance of trackers in the commercial realm and achieves state-of-the-art trade-off between speed and performance when paired with BYTETrack, as evaluated by IDF1 (a mixture between precision and recall) on common commercial tracking dataset MOT17.&nbsp;
The chart above demonstrates that ScaleTrack (packaged as our post-processors paired with BYTETrack) achieves a better speed/performance tradeoff than all other methods. BYTETrack and OC-sort are slightly faster, but significantly less performant than ours.&nbsp;
Why the emphasis on the comparison to BYTETrack (and not StrongSORT or SmileTrack)? Because any tracker that cannot be run at near-real time is nearly useless for Full Motion Video (FMV) workflows because most FMV users want algorithms that can be run as they review mission videos. And most cameras collect around 20FPS, meaning only the algorithms to the right of the red line can be of high value to FMV workflows.
<h3 dir="ltr">Federal results</h3>
The above graph is from evaluating an academic dataset. On mission-specific datasets, we outperform even more. As visual evidence of our outperformance over state-of-the-art in Federal datasets, please see the above MP4 sample results.&nbsp;
<h2 dir="ltr">How Did We Achieve These Results? (Technical Deep Dive)</h2>
<h3 dir="ltr">Reduction of flickering</h3>
Detectors basically never have 100% recall in the Federal domain. They often miss objects. Most trackers halt operations when this happens and kill the track. Certain trackers keep the object around in a state that is half dead/half alive for about 5 frames, and provide them the option of being revived if the object appears again.&nbsp;
We went with a different approach. We become skeptical of the detector when a track isn&rsquo;t propagated to the next frame and we try to persist the track by querying the image directly through a two-step process. This two-step process performs bounding box location prediction functions for a given track. The first prediction function is based on kalman information.&nbsp;
However, kalman filters fail sometimes. As a result, Scale did a thorough, detailed study of these failure cases and created a second, custom prediction function in bounding box search space. Given these bounding box hypotheses (from the kalman filter prediction function and our custom prediction function), we use a highly specialized visual feature function (which is not customer-locked or security-locked and therefore can be used by any customer at any security level) to select the bounding box which is most visually consistent with that track&rsquo;s visual history. Based on the level of visual consistency, we either persist the track to the next frame at that selected bounding box&rsquo;s location, or enter it into a state that is not yet dead but not entirely alive (i.e., a bank of tracks from which ReID can draw).
<h3 dir="ltr">Ability to maintain track ID's through short periods of erroneous track dropping</h3>
Sometimes, the flickering reduction mechanisms fail. But it&rsquo;s important that when the track shows up again, it maintains the same ID. So, for a set amount of frames after a track is last seen, we use its kalman filter to project the track over every frame. If there ever exists a new track that is visually consistent with the dead track and that new track exists in a location that is highly likely under the dead track&rsquo;s motion prediction distribution, we assign the dead track&rsquo;s ID to the new track. Thereby we would &ldquo;revive&rdquo; the old track in the new track&rsquo;s location, which allows us to maintain the track ID even though the object was missing for a short while. We refer to this as &ldquo;Local ReID.&rdquo; Outside of handling algorithmic false negatives, this also addresses short-term occlusions (e.g. a vehicle driving underneath an occluding tree).
<h3 dir="ltr">Ability to maintain track ID's through long periods in which the track wasn't present</h3>
This addresses the following use cases:
<ol>
<li dir="ltr" aria-level="1">
Objects coming in and out of the frame, potentially at an entry location significantly different from its exit location
</li>
<li dir="ltr" aria-level="1">
Objects that are occluded for long periods of time (e.g., a person coming in and out of a building)
</li>
<li dir="ltr" aria-level="1">
Camera zoom ins, zoom outs, and pans
</li>
</ol>
In these cases, traditional motion prediction breaks down, meaning traditional motion information prediction algorithms cannot be used to link new tracks with dead tracks. However, the objects rarely significantly change their looks within the span of a video. So, we introduce &ldquo;Global ReID&rdquo; which attempts to link dead tracks to new tracks strictly through a harsh visual filter.&nbsp;
<h3 dir="ltr">Ability to maintain track ID's through medium-length periods in which the track wasn't present</h3>
We further address use-cases 1 and 2 in the above section using a ReID function that is based on custom motion functions mixed with the above mentioned visual filter with time between dead and new tracks as a salient input to the mixture function. We do so in such a way that the larger the gap between these tracks, the less the linking function relies on spatial information and the more it relies on visual information, as there is increased uncertainty in motion as the time gap becomes larger.
&nbsp;

Assign accurate track ID’s and improve performance on top of aerial object detector outputs with video inputs

Scale's Solution for Aerial Object Tracking | Scale AI

Scale's Solution for Aerial Object Tracking

I am excited to join Scale as the General Manager, Public Sector. Over the past 25 years, I have dedicated myself to advancing public and private sector partnerships and leading transformational efforts that bridge the gap between the leading technologists and our federal users who need it. I am thrilled to embark on this new chapter with a company that shares my passion for leveraging cutting-edge technology to help the public sector adapt to the rapidly evolving technological landscape. As a former federal officer who served in far-flung areas of the globe, the work that Scale is accomplishing now is something we had dreamed of a decade ago.&nbsp;We are now living in that future.&nbsp;
Having served with the U.S. Army and Intelligence Community, I have witnessed firsthand the data challenges faced by the United States&ndash;from the tactical to the strategic. They all had one thing in common: our analysts, operators, and decision-makers could be better served by better data. Even two decades ago, my colleagues warned that they would have a problem with understanding, processing, and using the data generated by our forces at scale. And that is the challenge that Scale is solving today. Personally, Scale&rsquo;s commitment to supporting the U.S. government resonates with me, as I come from a family with four generations of public service. I am eager to collaborate with the U.S. government in delivering mission-critical, essential technology from the most innovative technologists.&nbsp;
Scale first caught my attention as a leader in the AI field when I heard about its own <a href="https://scale.com/blog/ukraine-detection" target="_blank" rel="noopener">battle damage</a> analysis of Ukraine; I was so impressed with the scope and the speed of work that was being accomplished on the front lines. Scale&rsquo;s computer vision machine learning is now also complemented by its large language model offering&nbsp;<a href="https://scale.com/donovan">Donovan</a>, Scale&rsquo;s federal-focused AI-power decision-making platform. I have always been a staunch advocate for transformation and innovation grounded in feedback from the end users. I am determined to drive forward efforts at a company that delivers this technology at scale&ndash;pun intended. The immense potential&ndash;and reality&ndash;of what Scale is doing today truly inspires me.
In particular, Scale&rsquo;s credo &ldquo;why not faster&rdquo; deeply resonates with me because the need to adapt and maintain the United States&rsquo; strategic advantage on the global stage has never been more urgent. Additionally, Scale&rsquo;s impressive capabilities&ndash;and its ability to deliver them securely&ndash;is bolstered by its incredible talent. I take great pride in the fact that Scale&rsquo;s employees, leaders and investors value service and support for the public sector.
 Join me at <a href="https://scale.com/careers">https://scale.com/careers</a>.

John joined Scale as the General Manager, Public Sector to accelerate the development of AI applications 

Why John Brennan joined Scale as General Manager, Public Sector

Why I Joined Scale

After the end of WWII and the invention of the atomic bomb during the Manhattan Project, J. Robert Oppenheimer gave an important speech reflecting on the technology he had just built, and in particular, the quantum and irrevocable impact it had on the nature of war:
&ldquo;It is clear to me that wars have changed.
It is clear to me that if these first bombs&mdash;the bomb that was dropped on Nagasaki&mdash;that if these can destroy ten square miles, then that is really quite something.
It is clear to me that they are going to be very cheap if anyone wants to make them.
It is clear to me that this is a situation where a quantitative change, and a change in which the advantage of aggression compared to defense&mdash;of attack compared to defense&mdash;is shifted, where this quantitative change has all the character of a change in quality, of a change in the nature of the world.&rdquo;
These words have great relevance today with AI. We are in the midst of the next great sea change in the nature of war, and thus, the nature of the world.
We live in uncertain times. Geopolitical risk is at&nbsp;<a href="https://www.matteoiacoviello.com/gpr.htm">a 20-year high</a> since the Ukraine war. The United States is facing <a href="https://www.jcs.mil/Media/News/News-Display/Article/613868/dempsey-us-forces-must-adapt-to-deal-with-near-peer-competitors/">near-peer competitors</a> in China and Russia for the first time since the end of the Soviet Union. There is a global battle for ideologies between Western democracy and authoritarian regimes.
All at once, the advent of AI has upended the world. It&rsquo;s a paradigm shift that has granted new superpowers and anything seems possible&mdash;with potentially greater implications on the future of our world than even the internet.
As someone who has been building in the AI ecosystem for more than seven years now, it&rsquo;s exciting to see this technology finally reach its watershed moment. But at the same time, this technology will exacerbate the uncertainty of our times. AI has the potential to unseat incumbents, catapult new leaders, or solidify existing moats. Navigating this new landscape is an existential matter&mdash;for many organizations, for our nation, and for democracy.
The United States, and the democratic world broadly, is confronted with two very real challenges.
First, as I&rsquo;ve <a href="https://alexw.substack.com/p/war?utm_source=profile&amp;utm_medium=reader2">discussed</a> previously, I believe the next era of war and deterrence will be defined by AI. We must ensure the strength of democracy in our AI war against authoritarianism by building the most effective military implementations of AI.
Second, a show of force is necessary, but not sufficient. At the end of the day, the strength of democracy has, and always will, stem from a stronger and faster-growing economy. That&rsquo;s why it is critical that we also bolster our global industries and enterprises with best-in-class capabilities.
The new Scale Donovan and Scale EGP platforms are our answer to these critical challenges&mdash;to empower the U.S. government and enterprises, respectively.
<h2 dir="ltr">Donovan &mdash; Ammunition in the AI War</h2>
AI will define the next era of war and deterrence, just as the atomic bomb did for the last era. The only solution to the AI War is to accelerate our own military implementations and rapidly integrate AI into warfighting. That's why we've funded our own R&amp;D to build Donovan&mdash;and achieve AI overmatch.
<a href="https://scale.com/donovan">Scale Donovan</a> is an AI-powered decision-making platform to help operators understand, plan, and act in minutes instead of weeks. Donovan ingests and understands vast amounts of structured and unstructured data to enable operators and analysts to make sense of any aspect of the real world in minutes using simple natural language. Donovan combines these insights with Scale&rsquo;s proprietary and state-of-the-art models to generate summary reports, COAs, and more to force-multiply staff bandwidth and achieve mission success.
As a longtime leader in AI, I am proud to be delivering this world-changing capability to the United States government with Donovan - the first LLM deployed on a classified network for the XVIII Airborne Corps - ingesting over 100,000 pages of live data to enable actionable insights across the battlefield. The sheer volume of inputs from various formats is a key challenge facing analysts and operators across the federal space. We cannot let the proliferation of information and manual processing be our Achilles heel.
<div class="wistia_responsive_padding" style="padding: 56.25% 0 0 0; position: relative;">
<div class="wistia_responsive_wrapper" style="height: 100%; left: 0; position: absolute; top: 0; width: 100%;"><iframe class="wistia_embed" title="Scale - Donovan Video" src="https://fast.wistia.net/embed/iframe/qugojsb52u?videoFoam=true" name="wistia_embed" width="" height="" frameborder="0" scrolling="no" allow="autoplay; fullscreen"></iframe></div>
</div>
<h2 dir="ltr">EGP &mdash; Unlocking Generative AI for Enterprises</h2>
<a href="https://scale.com/generative-ai-platform">Scale Enterprise Generative AI Platform (EGP)</a> is a full-stack solution for any company looking to deploy Generative AI applications in production. We are helping our customers - like Koch Enterprises - move rapidly from just experimenting with this technology to actually capturing business value from it, through a secure, enterprise-grade platform that is model-agnostic and powered by the industry's leading&nbsp;<a href="https://scale.com/data-engine">data engine</a>.&nbsp;
While Generative AI has captured the imagination of consumers, there are substantial safety, security, and performance challenges when it comes to business use cases. EGP allows customers to develop and deploy Generative AI applications within Scale&rsquo;s infrastructure or in their own virtual private cloud (VPC), so proprietary data never has to leave customers&rsquo; environments. Scale is the only provider delivering Generative AI applications in their own environment, coupled with industry-leading capabilities like Reinforcement Learning with Human Feedback (RLHF) and red-teaming to ensure that model outputs are aligned with customers&rsquo; policies and brand.
Further, EGP powers AI applications across industries. Think copilots for financial advisors to draw upon a much broader range of information when they are educating their clients, or tools for creatives to generate infinitely customizable images and copy, or a chat advisor to guide customers seeking information about retail products. These applications are only the tip of the iceberg - with EGP, companies are empowered to build and develop their own highly specific use cases. By bringing together Generative AI models with their own proprietary data, businesses can unlock new channels for growth and provide instant insights for better, faster decision-making.&nbsp;
<h2 dir="ltr">Looking Ahead</h2>
Right now, we&rsquo;re seeing a lot of AI tourists pretending to be AI natives, taking companies for a ride while they&rsquo;re at their most vulnerable. They&rsquo;re not selling solutions, they&rsquo;re selling vaporware. Everywhere you look, you&rsquo;ll see non-AI companies rebranding and selling technology that they only began to study yesterday. They barely have the knowledge internally to build the products they&rsquo;re selling, and they sure as hell don&rsquo;t have the expertise required to help customers build and implement a long-term, defensible AI strategy.
It&rsquo;s a problem that I care deeply about because the last thing the AI industry needs is more hype. And companies that are claiming AI expertise who haven&rsquo;t spent years in the trenches have the potential to do more harm than good.&nbsp;
The reality is that it takes a lot of hard work to go from experiment to enterprise. Foundation models aren&rsquo;t like a typical consumer software product that works out-of-the-box. From explainability and auditability to PII and proprietary data, there are several critical challenges businesses must address to make AI a reality. There is no established roadmap to follow, which is why only <a href="https://scale.com/ai-readiness-report">21% of enterprises</a> actually have AI models in production. In the federal government, only <a href="https://saic.com/sites/default/files/2023-01/SAIC-Cloud-WP-2023.pdf">14% of executives</a> believe they are &ldquo;very&rdquo; ready for AI.&nbsp;
It's clear that governments and businesses need a partner that provides battle-tested tools and expertise to harness Generative AI in a way that&rsquo;s trusted, secure and moves at the speed of the world around us today. Our new Scale EGP and Scale Donovan platforms were built to solve this problem.
The future of our world hangs in the balance of the current moment. AI has the potential to strengthen global democracies and democratic economies&mdash;or be the decisive implement that enables authoritarianism to take over.
We choose to take action&mdash;in light of all that is at stake&mdash;and build.
&nbsp;

Scale is proud to introduce our two Generative AI platforms – Donovan and EGP – to empower the U.S. government and enterprises to harness AI in a way that’s trusted, secure and moves at the speed of the world around us today.

A letter from our CEO | Scale AI

A letter from our CEO

Generative AI is capturing the imaginations of consumers and businesses alike. However, in this early era of adoption, most enterprises are yet to unlock true impact from this technology and are stuck in the experimental stages. Some are concerned about the security of their data and the reliability of model outputs. Others are overwhelmed by the many decisions to make regarding foundation models, prompt strategies, and tooling for developers.&nbsp;
Scale and <a href="https://www.anthropic.com/" target="_blank" rel="noopener">Anthropic</a> are excited to announce our partnership to bring to market a full-stack solution for Generative AI applications&nbsp;with a focus on safety and responsibility. This collaboration enables customers to bring Generative AI applications to production and to capture business value from this incredible technology.&nbsp;
Our partnership combines <a href="https://www.anthropic.com/product" target="_blank" rel="noopener">Anthropic&rsquo;s Claude</a> &ndash; a large language model guided by principles with the goal of creating outputs that are more consistent with human intent and values &ndash; with <a href="https://scale.com/generative-ai-platform">Scale&rsquo;s enterprise-grade platform</a> for app experimentation, development, and deployment.&nbsp;&nbsp;
Customers will be able to make the most of Claude with Scale&rsquo;s offerings:
<ul>
<li dir="ltr" aria-level="1">
Expert prompt engineering to guide users with the right prompt strategies and pre-built prompt templates, plus the ability to run experiments with different prompts.
</li>
<li dir="ltr" aria-level="1">
Data connectors to leverage proprietary data from databases and applications such as Confluence, Google Drive, Outlook, etc.
</li>
<li dir="ltr" aria-level="1">
Model validation through human evaluation to understand model blind spots and improve model performance, and features to continuously measure accuracy, brand alignment, and safety metrics, including sensitive content detection tailored to customers&rsquo; policies and brand.
</li>
<li dir="ltr" aria-level="1">
Enterprise-grade privacy and security, including deployment in a customer&rsquo;s AWS VPC and role-based access controls to ensure data and outputs are limited to the appropriate groups.
</li>
</ul>
Scale and Anthropic believe this represents a significant step forward for enterprises seeking to rapidly move beyond experimentation with Generative AI models. Designed to address concerns around data security, and model performance and reliability, this collaboration provides an accessible, &ldquo;all-in-one&rdquo; approach for customers to confidently tap into the power of Generative AI.

Scale and Anthropic are excited to announce our partnership to bring to market a full-stack solution for Generative AI applications, with a focus on safety and responsibility. This collaboration enables customers to bring Generative AI applications to production and to capture business value from this incredible technology. 

Scale + Anthropic: Full-Stack Generative AI for Enterprises | Scale AI

Scale + Anthropic: Full-Stack Generative AI for Enterprises

Scale AI, the data infrastructure for AI, has earned Impact Level 4 (IL-4) provisional authorization from the Defense Information Systems Agency (DISA), a significant milestone in Scale&rsquo;s ongoing commitment to providing the DoD with secure, industry-leading technology. This authorization allows Scale&rsquo;s cutting-edge machine learning (ML) platform and large language models (LLMs) access to defense applications with controlled unclassified information (CUI), such as personally identifying information, empowering the DoD with advanced AI capabilities to accelerate information accessibility. Scale is currently the only dedicated AI provider with IL-4 provisional authorization, underscoring its commitment to providing secure solutions for the U.S. government.&nbsp;
Scale&rsquo;s IL-4 provisional authorization expands AI accessibility for the DoD, allowing the agency to leverage Scale&rsquo;s Data Engine to curate and annotate data for a wide range of applications as well as Scale&rsquo;s Government AI platform for end-to-end solutions. Scale&rsquo;s powerful AI capabilities can empower analysts, operators, and leaders across the DoD with faster decision-making, improved operational efficiency, and accelerated innovation.
DISA, a combat support agency of the U.S. Department of Defense, is responsible for developing and maintaining the DoD Cloud Computing Security Requirements Guide (SRG). The Cloud Computing SRG defines the security requirements used by the DoD to assess the security posture of cloud service offerings, enabling cloud service providers (CSPs) to host DoD missions after receiving provisional authorization.&nbsp;
Scale&rsquo;s achievement of IL-4 provisional authorization demonstrates its adherence to rigorous security standards set forth by DISA and its commitment to supporting the DoD&rsquo;s mission-critical operations. With this achievement, Scale looks forward to unlocking the full potential of data and AI in the national security sector. For more information, please contact us at <a href="mailto:federal@scale.com">federal@scale.com</a>.&nbsp;
&nbsp;

Scale’s IL-4 provisional authorization expands AI accessibility for the DoD, allowing the agency to leverage Scale’s Data Engine to curate and annotate data for a wide range of applications as well as Scale’s Government AI platform for end-to-end solutions. 

Scale AI, the data infrastructure for AI, has earned Impact Level 4 (IL-4) provisional authorization from the Defense Information Systems Agency (DISA), a significant milestone in Scale’s ongoing commitment to providing the DoD with secure, industry-leading technology. 

Scale’s DISA IL-4 Provisional Authorization Increases Accessibility to AI for the Department of Defense (DoD) 

As we close out Women's History Month, we reflect on the incredible contributions of women throughout history and their importance in shaping our world today. In particular, we want to highlight the vital role that women have played and continue to play in STEM (Science, Technology, Engineering, and Math) fields.
Despite facing numerous challenges and barriers, women have made significant strides in STEM. From <a href="https://www.computerhistory.org/babbage/adalovelace/">Ada Lovelace</a>, the world's first computer programmer, to <a href="https://www.nasa.gov/content/katherine-johnson-biography">Katherine Johnson,</a> who helped put the first human on the moon, women have been pioneers in fields traditionally dominated by men. However, there is still much work to be done to address the gender gap in STEM, particularly in terms of representation and pay equity.
Throughout this month, Scale&rsquo;s Women ERG spoke to a few of its members to showcase the perspectives and experiences of women working in AI. We heard from Scaliens who have consistently broken barriers and achieved success in their fields, as well as those who have faced challenges and obstacles along the way.
We hope that by sharing these stories, we can help amplify the importance of diversity and inclusion in STEM and inspire the next generation of female leaders in the field. So, join us as we celebrate Women's History Month and recognize the incredible contributions of women in STEM. Together, let's continue to work towards a more equitable and inclusive future for all.
<img src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/503f7e9b-f8aa-4580-a096-2cba1e2402d6">
&ldquo;Having it all&rdquo; as a woman means different things to different women. I think this is important to acknowledge as I discuss what it means to me.
Last year, I began pursuing my MBA on top of my full-time job. When I first started, I quickly discovered that my definition of &ldquo;having it all&rdquo; was exhausting and unsustainable in my new season of life. It was time to change that definition.
Oftentimes women tend to take on more responsibilities both at work and at home. This was exactly what I was doing in every aspect of my life. It forced me to prioritize the limited free time I have. I had to set boundaries in my work and personal life to make room for school. I highly recommend The Book of Boundaries: Setting the Limits that Set You Free by Melissa Hartwig Urban as it was immensely helpful for me.
&ldquo;Having it all&rdquo; not only means different things to different women. It also means different things in different seasons of life. In my current season of life, &ldquo;having it all&rdquo; means shifting priorities and setting boundaries that help me succeed where I am today. When I finish my MBA, I&rsquo;ll enter a new season of life where &ldquo;having it all&rdquo; is going to evolve. And I can&rsquo;t wait to see what that looks like.&nbsp;
Yuri Maruyama
Head of Product Marketing
I joined Scale four and a half years ago when Scale was a forty-person company. At the time, I didn&rsquo;t think it would be possible for me to start a family while working at a hyper-growth, early-stage startup. It took a few years but I welcomed my first baby last summer. I was able to take and enjoy my full parental leave without getting pulled into any work or feeling any guilt.&nbsp;
Once I came back, I worried about how I would balance being back to work full-time while also being present for my family. I still often fall short of my own expectations, but to manage this, I&rsquo;ve blocked off my calendar in the mornings until 8:30am and in the evening between 5-8pm to spend with my family. Blocking this time and holding this boundary has helped to make me more present and productive at work during the 8:30am-5pm time when I am 100% focused on work. At the same time, I&rsquo;ve been able to be present for my family in the mornings and evenings without feeling guilty or like I need to check slack or email.
Bihan Jiang
Product Manager
Balancing work and life is both a challenge and a necessity. One way that I&rsquo;ve found balance is by scheduling time for activities that I enjoy, such as exploring coffee shops (I drink 3+ cups a day!), traveling, watching dramas, and playing board games like Catan and Tractor.
It&rsquo;s also critical to find passion in your work. As a product manager on the Spellbook team, I&rsquo;m responsible for driving the development of our product, collaborating with cross-functional teams, and staying on top of industry trends and customer needs. It can be challenging, but it&rsquo;s also incredibly exciting to be building in the generative AI space, which is evolving every day.
Achieving a balance between work and life is a continuous effort that requires prioritization. By making sure to allocate time for my hobbies and interests, as well as having a support system in place, I am able to maintain a strong sense of self while excelling in my career.
Karena Zhang
Account Executive
Passionate about living as a true &ldquo;Corporate Athlete,&rdquo; I find a myriad of ways to stay active and challenge myself in the office and beyond. When I first began my career, I was a Reformer Pilates instructor part-time while working full-time in tech. Now that I have since retired from being a fitness instructor, I have taken on a new challenge of traveling the world by way of sliding down mountains on wood planks (skiing). I was also a competitive college tennis player at Carnegie Mellon, so I am not new to navigating the stress of work and play, but I know how important it is to have a strong sense of self outside of work. I believe that no matter how stressful things get at work, I still have to live my life. Calendar blocks plus a passion for planning allow me to balance working hard, having fun, and making history.
<img src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/f383b465-2282-480b-bdb2-246cb1198506">As a female engineer, I have experienced being overlooked or underestimated many times in my career. Most of the time, there's nobody to blame--these biases are just that deeply ingrained in everyone. Over time though, I have learned to use this to my advantage. When my coworkers had low expectations of me, as long as I still set ambitious goals for myself, I could make sure I blew their expectations out of the water. I constantly remind myself daily to be confident in myself and to not let my goals and abilities be dictated by the male-dominated environment I'm surrounded by. I also keep in mind that as a minority in the workplace, I can bring fresh perspectives to the table. Finding success in engineering definitely feels like an uphill battle sometimes, but that also makes success feel much more rewarding.
Iris Guo
Strategic Product Manager
Despite lots of excitement and eagerness, I didn&rsquo;t know what to expect coming into the role of a strategic product manager as a new grad. In the past, I had more traditional product manager internships at big tech companies where I was building software/ hardware features and focused on the user experiences. I haven&rsquo;t done operations before and this was my first time getting into the weeds of running a project as an STO. As a woman with high degrees of agreeableness and conscientiousness, I found it difficult to be the one calling the shots and not asking for approvals before execution. At first, I thought I had to come up with the solution for every problem, so I would put a lot of pressure on myself to do everything right and I wasn&rsquo;t used to asking for help since I didn&rsquo;t want to bother other people. I tend to be detail-oriented and diligent in my tasks, but sometimes I would miss out on the bigger picture for strategic thinking. When I was stressed out at work, I would lean into my passion for dancing as a creative channel for me to destress. Dancing helped me express my full sense of self and relieved my obsessive thoughts from work to stay present. I performed ballroom dancing growing up and I started learning k-pop/ jazz funk/ contemporary/ bachata/ salsa when I moved to SF six months ago. Every group dance required lots of teamwork to help each other to learn the harder part of the dance, and I learned to ask for help from people with more experiences, which I applied to problems at work. Through dancing, I also learned to experiment and iterate fast, because we would have different versions of the same dance and improve from trials and errors. Dancing enlightened me to see the bigger pictures and let go of the unimportant details with bold movements, which served as a constant reminder to me at work. I felt extremely grateful for having a healthy and mutually beneficial relationship between my hobby and my job, and I believe that everyone can find the right balance between work and life.
<img src="https://drive.tiny.cloud/1/0lbhu31lrngl0y5eo8e6p0jckqgeikc94mnkaig43yuahupv/5ac7d1f9-b882-43ac-9da9-8511d5062f95">I&rsquo;m thrilled to be a woman and working with other talented men and women here at Scale as a part of the AI revolution. As a society, we have a ways to go in terms of representation and equity across all STEM fields, and think that women have an especially important role to play in bringing diverse perspectives shaping AI products, and ensuring that the future of AI is just, equitable, and serves all of humanity better.

We hope that by sharing these stories, we can help amplify the importance of diversity and inclusion in STEM and inspire the next generation of female leaders in the field.

Throughout this month, Scale’s Women ERG spoke to a few of its members to showcase the perspectives and experiences of women working in AI.

Women’s History Month at Scale

Artificial Intelligence (AI) has been a game-changer for everyone. Together with data, AI has become a key component of a robust digital core&mdash;the main source of competitive advantage for companies today. The advent of&nbsp;<a href="https://www.accenture.com/us-en/insights/technology/generative-ai" data-analytics-link-name="generative ai" data-analytics-content-type="cta">generative AI</a>&nbsp;marks the beginning of a new era, one where businesses&nbsp;<a href="https://www.accenture.com/us-en/insights/consulting/total-enterprise-reinvention" data-analytics-link-name="reinvent" data-analytics-content-type="cta">reinvent</a>&nbsp;how they work. Just search for ChatGPT if you haven&rsquo;t already, and you&rsquo;ll get an idea of the possibilities. ChatGPT is derived from a large language model (LLM)&mdash;GPT4&mdash;developed by Open AI, which is a foundation model capable of generating human-like language and carrying out complex tasks across a number of functions.
We are entering an era of AI industrialization, where entire industries stand to be transformed by new, innovative applications of generative AI. One of the highest barriers to unlocking business value using generative AI and LLMs, however, is customizing and fine-tuning the pre-trained foundation models for their unique needs, using their own data. That&rsquo;s why Accenture is partnering with Scale AI.&nbsp;
<a title="Scale AI &amp; Accenture Partnership" href="https://scaleai.wistia.com/medias/2fjp89kejj">video</a>
&nbsp;
<h3 class="p1">Generative AI partner spotlight: Scale AI</h3>
Accenture&rsquo;s collaboration with&nbsp;<a href="https://scale.com/" target="_blank" rel="noopener" data-analytics-link-name="scale ai" data-analytics-content-type="cta">Scale AI</a>&nbsp;focuses on helping enterprises use their own data on general purpose models, customizing them to suit their business needs. According to CEO and founder Alexandr Wang, Scale AI has been &ldquo;quietly powering this generative AI wave, providing the data and infrastructure for many of the top companies to actually build large-scale foundation models, including Open AI.&rdquo;
Accenture and Scale AI are collaborating to provide an enterprise LLM platform for enterprise and government clients to fine-tune generative AI models that can learn from human input through a process called Reinforcement Learning from Human Feedback (RLHF). The result is generative models optimized for human interaction that are able to improve how people live and work.
For example, Scale AI was able to help retail clients gain higher shopping conversion and sales by using AI-generated product images for their newest products. And with social media clients, AI-generated content led to more than 100 million impressions.
Could humans have done this? Absolutely &ndash; but at what cost to the business?
This is business augmentation at the intersection of human and machine. By freeing up employees from automatable, routine tasks, we can redirect energy and focus to more strategic creative work that requires human intuition, decision-making and problem-solving skills - work that generative AI cannot do&mdash;at least not yet. The combination of human plus machine results in more efficient processes, improved decision-making and greater innovation.
Success with generative AI depends as much on people and training as it does on technology. It&rsquo;s up to business leaders to take the lead in upskilling employees and reshaping jobs and tasks to get the most out of technologies like generative AI.
Thanks to partnerships like this one with Scale AI, Accenture helps leaders create business value with generative AI technology quickly and at scale.

Scale AI + Accenture: Enhancing Business Value with Human Ingenuity

open-dataset-nucleus

slugs must be all lowercase. ie: ‘my-slug’

events-banner

This entry represents both interview quote highlight and interview chat highlight because of Builder limitations. Choose the fields that matches either of them.

interview-highlight

If it's selected, the author and quote fields will be taken, otherwise the chat fields. The default is for chat.

resources-download-model

transform-session

CTA to open the session. Leave blank if you don't want a cta for that session

Link for the CTA. Usually the link to the exchange session

blog-filter-type

event-transform-x-ticket

solution

homepage-quotes

logos

homepage-content

builder-debug

transfrom-x-in-person

event-model

Toggle if the event is featured. Featured events will be moved to the top of the list

guides-cta

summit-2023-partners

sales

z[deprecated] page

quotes

z[deprecated] compare

product

blog-category-model

announcement-banner

accelerate

Vercel Powered and Edge Computed OG Image. Preferred over og img file upload. If this field is non empty, it will take precedent over the uploaded file

summit-2023-speakers

homepage-resources

customers

Display order on the page /customers. Higher values show first

page-cross-product-webinar

interview

event-transform

Event model for new marketing events page

event-2-0

homepage-event

custom-fonts

transform-x-speaker

Be careful to not add any trailing white spaces to the slug. Slugs must be all lowercase. ie: ‘my-slug’

Order of the featured speakers displayed on the home page for transformX

z[deprecated] document-ai

summit-2023-agenda

event-converge

hosts

author

transform-x

Page: /In-Person. Section: Hero. Text: Title Text

Page: /In-Person. Section: Slider Card. Text: Title Text

Page: /In-Person. Section: Slider Card. Text: Card Body

Page: /In-Person. Section: Hero. Text: Tagline

open-dataset

z[deprecated] customers

URL redirections for Website. Disclaimer: takes 5~ min for each added or edited.

url-redirects

Use relative path. use `/blog/foo` not `https://scale.com/blog/foo`

legal

event

Optional second image intended to show on the grid view, should be a more squared image closer to 1:1 aspect ratio than the one used in the Cover field.

guides

z[deprecated] guides-old

z[deprecated] guides-lp

partners

blog-model

Option to list or not list this post in the Blog index. Enabled = not list in the index, Disabled = list in the index

Products

Government

Solutions

Resources

Customers

Pricing →

Blog

The future of your industry starts here.