Orca
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BUSINESS 2 Another Level.
TRANSCRIPT
0:00
Microsoft has released a new research
0:02
paper on Orca a revolutionary AI model
0:05
that learns from complex explanations of
0:07
gpt4 the most powerful language model in
0:10
the world this is a huge deal and I'm
0:13
going to tell you why in this video so
0:15
what is orca and why is it so important
0:18
well Orca is a 13 billion parameter
0:21
model that learns from complex
0:22
explanation traces of gpt4 which is a
0:25
much bigger model that can generate
0:27
almost any kind of text you can imagine
0:29
now why would Microsoft want to create a
0:31
smaller model that learns from a bigger
0:33
model isn't bigger always better when it
0:36
comes to AI well not exactly bigger
0:38
models are more powerful but they also
0:40
have some drawbacks they are very
0:42
expensive to train and run they require
0:45
a lot of computing resources and energy
0:47
and they are not very accessible to most
0:49
researchers and developers that's why
0:52
there has been a lot of interest in
0:53
creating smaller models that can still
0:55
perform well on various tasks such as
0:58
answering questions summarizing texts
1:00
generating captions and so on these
1:03
smaller models are usually fine-tuned on
1:05
specific data sets or instructions to
1:08
make them more specialized and efficient
1:11
however there is a problem with this
1:13
approach smaller models tend to have
1:15
poor reasoning and comprehension skills
1:17
compared to bigger models they often
1:19
make mistakes or give irrelevant answers
1:21
when faced with complex or ambiguous
1:23
queries they also lack the ability to
1:26
explain how they arrived at their
1:27
answers or what steps they took to solve
1:30
a problem but Orca is not just another
1:32
smaller model that imitates a bigger
1:34
model Orca is a smaller model that
1:36
learns from the reasoning process of a
1:38
bigger model it learns from the
1:40
explanations that gpt4 gives when it
1:43
generates its answers these explanations
1:45
are not just simple sentences or phrases
1:47
they are detailed traces of how gpt4
1:50
thinks step by step how it uses logic
1:53
and Common Sense how it connects
1:55
different pieces of information and how
1:57
it simplifies complex concepts by
1:59
learning from these explanations Orca
2:01
becomes much more capable and
2:04
intelligent than other models it can
2:06
handle more diverse and challenging
2:07
tasks it can give more accurate and
2:09
relevant answers and it can also explain
2:11
its own reasoning process to humans this
2:14
is a huge breakthrough for open source
2:16
AI Orca is set to be open source soon
2:18
which means anyone will be able to use
2:20
it and build upon it it will enable more
2:22
people to access the power of gpt4
2:25
without having to pay for it or deal
2:27
with its limitations Orca will also open
2:30
up new possibilities for AI research and
2:32
development especially in areas that
2:34
require more reasoning and understanding
2:37
skills to understand how Orca works we
2:39
need to First understand how gpt4 works
2:42
so gpt4 is more than a text generator it
2:46
performs tasks requiring reasoning like
2:49
answering factual questions summarizing
2:51
lengthy texts generating captions
2:54
writing essays and more interestingly
2:57
gpt4 can provide explanations for its
3:00
outputs these are found in the model's
3:01
internal States essentially its thoughts
3:04
or memories which hold the logic and
3:07
information used to generate outputs by
3:09
using specific prompts we can unveil
3:12
these internal explanations giving a
3:14
detailed view of how gpt4 thinks solves
3:17
problems and uses diverse sources of
3:19
information including its own memory the
3:22
web and Common Sense these explanations
3:24
are very valuable for smaller models
3:26
that want to learn from gpt4 they
3:29
provide more signals and guidance for
3:31
how to perform various tasks and how to
3:33
reason and understand different concepts
3:35
they also make the learning process more
3:37
transparent and interpretable for humans
3:39
this is what Orca does Orca learns from
3:42
these explanations that gpt4 generates
3:45
when it performs different tasks it uses
3:47
these explanations as its training data
3:49
and tries to imitate them as closely as
3:51
possible Orca also tries to generate its
3:54
own explanations when it performs
3:55
similar tasks and Compares them with
3:58
gpt4's explanations to improve itself so
4:01
Orca is actually based on vicuna a
4:04
previous open source model that was
4:06
fine-tuned on question answer pairs from
4:08
GPT 3.5 Orca extends by kuna by adding a
4:12
new technique called explanation tuning
4:14
which allows it to learn from complex
4:16
explanation traces of gpt4 explanation
4:19
tuning is a Fresh Approach that enhances
4:21
gpt4's skill to follow specific
4:24
directives by refining this AI with
4:26
prompts like summarize this in a
4:28
sentence or create a love Haiku we make
4:31
it more Adept at particular tasks but
4:33
explanation tuning goes beyond it hones
4:35
gpt4 to reveal its thought process using
4:38
prompts like think sequentially or
4:40
explain like I'm a child this way gpt4's
4:44
reasoning becomes more transparent this
4:46
technique involves standard and
4:47
explanation prompts former our usual
4:50
tasks like who leads France or craft a
4:52
winter poem the latter instruct gpt4 to
4:56
clarify its logic like think in steps or
4:58
show how you did it using both prompt
5:01
types together gpt4 produces complex
5:04
explanation traces for instance with the
5:06
standard prompt who leads France and the
5:09
explanation prompt think in steps gpt4
5:12
might provide a step-by-step explanation
5:14
this comprehensive response not only
5:16
tells us who the president is but also
5:18
illustrates gpt4's problem-solving
5:20
strategy and information sources
5:22
offering more insight than a simple
5:24
answer Orca leverages explanation traces
5:26
as learning material striving to mimic
5:29
them and generate its own for
5:31
improvement but where do these traces
5:33
come from Orca Taps into flan 2022 a
5:36
massive collection of over 1 000 tasks
5:39
and 10 000 instructions covering a
5:41
spectrum of subjects by sampling from
5:43
flan 2022 Orca gets a variety of tasks
5:46
and uses them to query gpt4 for
5:49
explanation traces it also creates
5:51
complex prompts from the data set to
5:53
test gpt4's reasoning like mashing two
5:56
tasks into one this way Orca learns from
5:59
diverse and intricate tasks fostering
6:01
many aspects of human intelligence Orca
6:03
is evaluated on a number of benchmarks
6:06
that test its generative reasoning and
6:08
comprehension abilities these benchmarks
6:10
include multiple choice questions
6:12
natural language inference text
6:15
summarization text Generation image
6:17
captioning and so on Orca is compared
6:20
with other models of similar size or
6:22
larger size such as vikuna 13B text
6:25
DaVinci 003 a free version of gpt3 chat
6:29
GPT 3.5 and gpt4 orca's performance is
6:33
Stellar topping all other open source
6:35
models in most benchmarks particularly
6:38
those needing deeper reasoning despite
6:40
its smaller size it matches or beats
6:42
chat GPT in many areas even competing
6:45
with gpt4 in tasks like natural language
6:48
inference or image captioning
6:50
here's a quick look at orca's Benchmark
6:52
performances on big bench hard BBH it
6:56
scores a 64 accuracy more than double of
6:58
vicuna 13bs 30 and surpassing chat gpts
7:03
59 and gpt4s 62 on super glue SG it
7:09
achieves an 86 average beating vicuna
7:12
13B 81 Tex DaVinci 003 83 chat GPT 84
7:19
and nearly matching GPT 4 88 on CNN
7:24
daily mail CDM Orca earns a rugel score
7:27
of 41 outperforming vicuna 13B 38 text
7:32
DaVinci 003 39 chat GPT 40 and closing
7:38
in on GPT 4 42 on Coco captions CC it
7:43
scores a cider of 120 higher than vicuna
7:47
13B 113 text DaVinci 0.003 115 chat GPT
7:54
117 and GPT 4 119 so as you can see Orca
8:00
is a highly versatile efficient model
8:02
performing well across tasks and domains
8:05
and soon to be open source it also works
8:08
on a single GPU orca's success reveals
8:11
multiple insights about ai's future
8:12
firstly it indicates that learning from
8:15
explanations as opposed to just answers
8:17
notably boosts AI intelligence and
8:20
performance by studying gpt4's
8:23
explanations Orca not only gains
8:25
Superior reasoning skills but also
8:27
provides a transparent look into its
8:29
problem-solving process secondly Orca
8:31
proves that despite their size smaller
8:34
models can match or outperform larger
8:36
ones learning from gpt4 Orca side steps
8:40
size related drawbacks showing that
8:42
smaller models can be more approachable
8:44
and efficient needing fewer resources
8:47
and energy and thirdly orca exemplifies
8:50
how open source AI through inventive
8:53
methods can match proprietary Ai and
8:56
demonstrates how open source ai's wider
8:58
accessibility can benefit more people
9:01
and spur more applications concerning
9:03
its positioning Orca isn't just a mini
9:06
gpt4 or another open source model while
9:09
it doesn't match gpt4's broad capacity
9:12
or knowledge base it harnesses gpt4's
9:15
reasoning making it smarter than other
9:17
small models it also surpasses gpt4 and
9:20
transparency by generating its own
9:22
explanation traces unlike other open
9:24
source models Orca learns from a varied
9:27
range of tasks and complex explanations
9:29
making it more intelligent and versatile
9:31
therefore Orca occupies a unique
9:34
position in the AI sphere combining
9:37
gpt4's prowess with open source ai's
9:39
accessibility and demonstrating the
9:41
potential of explanation-based learning
9:43
alright that's it for this video thank
9:46
you so much for watching if you like
9:47
this video please give it a thumbs up
9:49
and share it with your friends and if
9:51
you haven't already please subscribe to
9:53
my channel and turn on the notification
9:54
Bell so you don't miss any of my future
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videos I'll see you in the next one
TRANSCRIPT
0:03
hey what is up guys welcome back to
0:05
another YouTube video at the world of AI
0:07
in today's video we're going to be
0:09
showcasing a new open source project by
0:11
Microsoft research and that is called
0:13
Orca it's a progressive learning project
0:15
from the complex explanation traces of
0:18
gpt4 now in this video we'll delve into
0:21
the new research paper which focuses and
0:24
introduces an Innovative technique for
0:26
training open source models now this is
0:28
quite remarkable guys because we're able
0:31
to see that there's open source projects
0:33
that are slowly but surely getting
0:35
closer to GPT 3.5 as well as gpt4 types
0:40
of results we can see in the suite right
0:42
here that it's surprisingly capable of
0:44
reaching GPT 3.5 turbo results in terms
0:48
of its performance capabilities and it's
0:50
basically able to do this by Computing
0:52
with no internet actual connection as
0:55
well as about eight gigabytes of hard
0:58
drive space and this is quite
1:00
astonishing because we're able to surely
1:02
get to this open source model that can
1:05
compete with chat gbc's 3.5 models as
1:08
well as gpt4 models and slowly but
1:11
surely we're able to see that there's
1:12
going to be a time where we're getting
1:14
closer to this point where open source
1:17
models can be able to actually compete
1:19
with these different types of GPT 3.5
1:21
models now this technique presents a
1:24
significant advancement by enabling
1:26
small models with a size of 13 billion
1:28
parameters to go beyond merely imitating
1:32
large foundational models such as GPT
1:34
3.5 and this is why we're going to be
1:37
showcasing this project as well as this
1:39
research paper
1:40
now the paper introduces a novella
1:43
approach that empowers these smaller
1:45
models to acquire reasoning capabilities
1:48
as well as well as which helps them
1:51
enable them to understand and
1:53
demonstrate the underlying thought
1:54
process behind arriving towards a
1:57
solution now this breakthrough is a
2:00
notable step forward in the field of
2:01
machine learning as it showcases the
2:04
enhancements of models capabilities that
2:06
can go beyond the pattern's recognition
2:08
Now by incorporating this new training
2:11
technique the researchers at Microsoft
2:13
research have been able to bridge the
2:15
gap between large foundational models
2:17
and smaller models which enables the
2:20
latter reasoning capabilities similar to
2:22
large counterparts and basically with
2:24
this development we're able to see that
2:26
it opens up a new possibility for
2:28
smaller models to tackle complex tasks
2:31
and provide more insightful responses
2:33
for different types of generative
2:35
content and this is something that will
2:37
slowly but surely Showcase in this video
2:40
we're going to talk a little bit about
2:41
the data that is used to create this
2:43
project as well as taking a look more as
2:46
to what you can do with this as well as
2:48
taking a look at the experiments of this
2:50
project and with that thought guys
2:52
before we delve deep into this project
2:54
it would mean the whole world to me if
2:55
you guys can go give Twitter page of
2:57
world of AI a follow I'm going to be
3:00
posting the latest AI news over here now
3:02
if you guys haven't subscribed please do
3:04
so turn on the notification Bell like
3:06
this video and firstly I just want to
3:08
say I am so sorry for not uploading for
3:11
the last few days you can see there's
3:14
been a trend where I haven't been
3:15
uploading daily after this two-week Mark
3:19
it's been really busy in June guys I've
3:22
been really hectic with a lot of
3:23
different projects as well as a lot of
3:25
different things so I'm gonna try my
3:27
best to upload daily but at this rate I
3:29
don't think I'll be able to do so in
3:31
within this month I just want to tell
3:33
you guys and be honest because I'm not
3:35
going to be able to work towards putting
3:37
out videos because I have a lot of
3:38
important stuff on my end that need to
3:41
complete but once that is done usually
3:44
around in July I'll be more free to
3:45
upload back to my regular schedule where
3:48
I upload two videos a day and with that
3:50
thought guys let's get right into the
3:52
video
3:54
now let's take a look at this figure in
3:57
the introduction now in figure one
3:59
there's the context of orca's research
4:01
project which demonstrates the superior
4:03
performance of orca now this is just
4:06
simply a 13 billion parameter model of
4:09
orca being compared to various
4:11
Foundation models including open ai's
4:13
chat gbt 3.5 as well as gpt4 now this
4:18
evaluation is conducted using by kona's
4:20
evaluation set now the results indicate
4:23
that orca's outperforming these
4:26
foundational models in terms of its
4:27
performance and accuracy on specific
4:30
evaluation sets now additionally to this
4:32
what the paper is showcasing is that
4:35
there's a similar favorable outcome when
4:37
testing Orca against evaluation sets
4:40
used in other research works it
4:42
indicates that there's General
4:43
Effectiveness across different scenarios
4:46
now moving on to the second figure we
4:49
can see that it focuses more on the
4:51
concept of explanation tuning with Orca
4:53
now again it utilizes a model with 13
4:56
billion parameter like the figure one we
4:59
showcased and basically this objective
5:01
is to close the performance gap between
5:04
Orca as well as the for foundational
5:06
models like text DaVinci or like
5:10
different ones such as orc uh Chaturbate
5:13
and mikona 13B and basically we're able
5:17
to see that's closing the Gap with these
5:19
different types of model now the
5:21
valuation is carried out with a diverse
5:23
range of professional and academic exams
5:25
such as lsats as well as SATs GMAT Etc
5:29
and notably this is the evaluation that
5:32
is conducted in a zero shot setting
5:34
meaning that there's no type of
5:36
conversation on the topic data that is
5:39
used to Aid the model's performance now
5:41
the results presented in this figure are
5:43
indicating that Orca achieves a
5:45
significant progression in closing the
5:48
performance Gap with the foundational
5:49
models all these different types of LMS
5:52
over here not specifically in comparison
5:54
the chat GPT Orca was able to
5:57
demonstrate a reduction in the
5:59
performance Gap by 5 Points and
6:01
furthermore it should be noted that the
6:03
Gap further diminishes when the system
6:05
message are optimized and this suggests
6:08
that the orca's explanation tuning
6:10
capabilities are able to enhance its
6:12
performance on professional and academic
6:14
exams which is kind of showcasing that
6:18
it can enable it to achieve competitive
6:20
results without the need for explicit
6:22
training as well as specific exam result
6:24
data now this is quite remarkable guys
6:28
because you can see that you're able to
6:29
train smaller models on Lower data sets
6:33
which can help you outperform or become
6:36
like you're able to be competitive with
6:40
these larger models which is quite
6:41
amazing as it showcases that you're
6:44
going to be able to efficiently and
6:46
effectively train different types of
6:48
models and this is something that will
6:50
surely showcase later on in the video
6:54
now before we actually go more deep in
6:57
depth with the other stuff in the video
6:58
where we talk a little bit more about
7:00
the actual analysis as well as
7:02
experiments I wanted to talk about the
7:04
challenges with existing models now for
7:07
now there's a problem with which we can
7:10
see with the existing models which is
7:12
interpretability and explainability in
7:15
complex machine learning models and
7:17
these are some of the several challenges
7:18
that are faced with these existing
7:20
models and these challenges include the
7:23
increasing complexity of models which
7:26
show that there's a lack of transparency
7:28
in decision making processes and the
7:31
difficulty in understanding the
7:32
underlying relationships between the
7:34
inputs and the outputs now additionally
7:36
to this there's a limited
7:37
interpretability which hinders the
7:39
identification of biases errors and
7:42
errors of uncertainty within the model
7:45
now the need for this method can be
7:47
addressed with challenges that are
7:49
crucial to enhance the trust the
7:52
transparency and accountability in the
7:54
deployment a complex machine learning
7:56
models across various domains and this
7:59
is something that they've noted down
8:00
with the Microsoft researchers which are
8:03
working on this project as some of the
8:05
challenges with existing parameters of
8:08
the actual models that exist
8:11
in this research paper there are
8:14
primarily four focuses that are
8:16
addressing the challenges that are
8:18
mentioned above and firstly we're going
8:21
to talk about the progressive learning
8:22
framework now we introduced this novel
8:26
Progressive learning method at the start
8:28
which is called Orca and it's basically
8:31
designed to enhance the interpretability
8:32
and the explainability in complex
8:35
machine learning models now this
8:37
framework enables the actual method to
8:39
like refine its understanding by
8:42
incorporating new explanation traces
8:44
over time and this is by accumulating
8:47
and analyzing a diverse range of
8:49
explanation traces and with Orca you're
8:52
able to see that there's a progressive
8:54
adaptation and a work towards improving
8:58
its interpretation interpretation sorry
9:00
of complex systems and this is one of
9:03
the key contributions to actually
9:05
working towards the challenges of
9:07
existing models secondly there's a
9:10
complex explanation traces
9:12
of emphasizing that it works towards the
9:16
importance of complex explanation traces
9:18
and understanding the decision-making
9:20
process of different types of models and
9:24
these traces basically provide a
9:25
detailed record of the model's internal
9:28
operations which focuses on including
9:30
data flow of computation and decision
9:34
actual outcome sorry now by leveraging
9:38
these traces orcas captures the actual
9:40
relationship between the inputs as well
9:43
as the actual outputs that are used
9:45
within the model and what this actually
9:48
does is that enables a deeper
9:50
understanding of the model's Behavior
9:52
now a third contribution is the actual
9:55
net enhancements of its interpretability
9:58
and through the use of orca we're able
10:01
to see that there's a progressive
10:02
learning and a complex explanation
10:04
traces which are used to demonstrate the
10:07
distinguished improvements of the
10:09
interpretability of complex machine
10:11
learning models now the insights that
10:14
are gained from accumulated explanation
10:15
traces helps enable to identify
10:18
important features which helps
10:20
understand decision boundaries as well
10:22
as highlighting areas of uncertainty and
10:25
biases within the actual model now this
10:27
interpretability enhancement contributes
10:29
to the building trust and understanding
10:31
in decision making processes of these
10:34
models
10:35
lastly the last key contribution is the
10:39
comparative performance evaluation now
10:41
with Orca it provides a comprehensive
10:43
comparator comparative evaluation that
10:46
is against various Foundation models
10:48
which we showcased above in this table
10:50
and basically the valuations demonstrate
10:53
that orcup is able to outperform these
10:56
models in terms of accuracy performance
10:58
and interpretability on different
11:00
evaluation sets as well as benchmarks
11:03
now this comparative analysis highlights
11:06
Effectiveness and robustness of orca and
11:08
complex scenarios and by addressing
11:11
these challenges that we talked about
11:12
we're able to see that there is an
11:16
introduction of towards the progressive
11:18
learning framework which emphasizes a
11:20
solution for a complex explanation
11:22
traces and enhancing interpretability
11:24
and conducting through performance
11:26
evaluations
11:28
now in table one we're able to see that
11:31
it overviews the popular models that
11:34
have been in like actually used for
11:36
instruction tuned with open ai's large
11:38
foundational models now these lfms serve
11:42
as a basis for comparison in the context
11:44
of Aura now in Orca sorry in contrast to
11:48
these models Orca has been able to
11:50
leverage complex instruction and
11:52
explanation for Progressive learning and
11:55
about utilizing detailed and
11:56
comprehensive explanations Orca was able
11:59
to enhance its learning process and
12:01
improving its interpretability now this
12:04
approach set showcases that orcas are
12:07
part of different types of models that
12:09
rely solely on instruction tuning with
12:12
showcasings showcases the unique
12:15
contribution and effectiveness of orca
12:17
in the domain interpretability of
12:19
machine learning
12:21
in figure four we're able to see that it
12:24
illustrates the process of instruction
12:26
tuning with gbt4 and this approach where
12:30
the system takes the user instruction
12:32
for a specific task along with the input
12:34
and generates an appropriate response
12:37
now existing works such as alpaca by
12:40
kuna as well as their variants follow a
12:42
similar template for training smaller
12:44
models and they utilize the training
12:47
data in the format of user instruction
12:49
input as well as output to fine-tune
12:52
these models now instruction tuning
12:54
involves training the models to
12:56
understand and follow user instruction
12:58
in order to generate accurate and
13:01
contextual appropriate responses Now by
13:03
incorporating both the user instruction
13:05
and the input these models tend to aim
13:08
to capture the desired Behavior or
13:10
responses from the system now we can see
13:13
over here that alpaca of ikuna as well
13:16
as similar approaches that have been
13:18
utilized to train smaller models
13:20
leveraged instruction tuning techniques
13:23
to improve the performance of
13:25
effectiveness of these smaller models
13:27
Now by training on specific user
13:30
instructions as well as Associated
13:31
inputs these models can learn to
13:34
generate responses that align with
13:36
intended tasks or approaches
13:39
now in context or Orca figure 4 is able
13:43
to highlight the difference in
13:44
approaches now while instruction tuning
13:47
with gpt4 or GPT 3.5 relies on user
13:50
instruction as well as user inputs Orca
13:53
takes a distinct path by leveraging
13:55
complex instructions and explanations
13:57
for Progressive learning and this is by
14:00
utilizing detailed explanations in
14:02
addition to instruction and basically
14:05
with Orca it enhances its learning
14:08
process and offers an improved
14:10
interpretability which sets it apart
14:12
from models that solely rely on
14:14
instruction tuning techniques
14:18
in figure 5 it depicts the process of
14:21
explanation tuning with gpt4 now this
14:24
approach involves not only user
14:26
instructions and inputs but also system
14:28
instructions which guides the system to
14:31
generate well-reasoned and coherent
14:33
responses now the system instructions
14:35
are sampled from a diverse set that
14:38
includes a Chain of Thought reasoning
14:39
steps as well as explaining like I'm
14:43
five now being helpful which shows other
14:46
relevant structures of this model Now by
14:49
incorporating these rich and
14:50
well-structured instructions small
14:53
smaller models that can be tuned to
14:55
mimic and thinking processes of gpt4 can
14:58
be paired with Orca now explanation
15:01
tuning with gbt4 aims to enhance the
15:03
quality as well as the coherence of
15:05
responses generated by smaller models
15:08
about providing an explicit system
15:10
instruction these models are guided to
15:13
produce while recent as well as
15:15
contextual appropriate outputs these
15:18
system have been able to Showcase that
15:20
there's instruction contributions to
15:22
capture the thinking process as well as
15:25
the reasoning abilities of larger GPT
15:27
form models by utilizing orca
15:30
lastly I want to emphasize a little bit
15:32
more on the explanation tuning and we'll
15:35
end off this video by showcasing a
15:37
couple examples of what you can do in
15:39
terms of orca in comparison to different
15:42
types of models such as GPT 3.5 turbo as
15:45
well as gpt4 now we talked about
15:48
training before but training plays a
15:51
vital role in the development and the
15:53
deployment of orca and this section
15:55
basically showcases an importance that
15:58
all reviews the training process
16:00
including tokenization and sequencing as
16:03
well as loss computation now code
16:05
tokenization and the training of orca as
16:08
well as the Llama by bit pair encoding
16:11
tokenizer is employed to process the
16:14
input examples and this is the notable
16:16
feature of llama's tokenizer which is an
16:18
ability to split all numbers into
16:21
individual inputs as well as digits
16:23
which ensures this numerical information
16:26
is appropriately representative within
16:29
its data now sequencing is used after
16:32
tokenization which basically focuses on
16:35
the inputs examples that are organized
16:37
into sequences and basically they're
16:40
suitable for training the actual model
16:42
and these sequences ensure that the
16:44
information is properly structured and
16:47
can be effectively processed by the
16:49
model during the training phase and
16:51
lastly there is the focus of loss
16:54
computation and this is during the
16:56
training process where the model
16:57
performance is evaluated through a
16:59
computation of loss and the loss is
17:02
basically a function that quantifies the
17:05
disparent sorry the discrepancy between
17:08
the predicted output of the model as
17:11
well as the desired outputs
17:14
now the experiments conducted by orca to
17:18
evaluate its performance for conducted
17:20
in-depth with a focus that provides
17:22
detailed insights into its capabilities
17:25
the evaluation encompassed a variety of
17:28
tasks and benchmarks to assess orca's
17:30
language understanding reasoning
17:33
abilities as well as its overall
17:34
performance now the experiments involve
17:37
the rigorous evaluations as well as
17:39
protocols that considered different
17:41
abilities including writing
17:43
comprehension analytical mathematical as
17:47
well as logical reasoning now various
17:49
data sets and benchmarks are actually
17:51
utilized to provide a comprehensive
17:53
evaluation of orca's ability to Showcase
17:57
its performance across different domains
17:59
now for writing abilities we're able to
18:02
see later on in the actual research
18:04
paper that it showcases its evaluation
18:07
where orca's responses were assessed by
18:10
coherence contextual relevance as well
18:13
as overall quality and this is something
18:16
that was compared with different types
18:18
of large models such as GPT 3.4 as well
18:22
as or sorry not 3.4 3.5 and gpt4 and the
18:27
model's Proficiency in generating
18:28
well-structured and informative
18:30
responses that were thoroughly exempt
18:33
with this comparison now in terms of its
18:36
comprehension the orca's understanding
18:39
of complex textual information as well
18:41
as its ability to accurately interrupt
18:44
and respond to comprehensive based
18:46
questions were assessed and this
18:48
evaluation was aimed to measure the
18:51
model's comprehension on terms of its
18:54
skills and its capabilities to derive
18:56
relevant information from given tasks
18:59
now if you want to get more information
19:00
on these types of experiments if you
19:03
scroll down a little bit to the end
19:05
you're able to get a better
19:06
understanding of these different types
19:08
of responses that were compared with
19:11
different larger models such as vicona
19:13
gpt4 and Etc and we can see down over
19:17
here there's temporarily a section that
19:21
focuses on the types of comprehensive as
19:24
well as the performances across
19:27
different domains such as math and as
19:30
well as the different things that we
19:31
talked about before and with that
19:33
thought this concludes today's video on
19:35
this new Amazing Project of orca I hope
19:38
you got some sort of value out of this
19:40
research paper this is something that
19:42
I'll be showcasing later on as they
19:44
continuously work towards improving as
19:47
well as incorporating different
19:48
Innovative tactics to improve This
19:50
research paper and with that thought
19:52
guys thank you so much for watching hope
19:55
you got some sort of value out of this
19:56
if you guys haven't followed the Twitter
19:58
page please do so if you haven't turn on
20:00
notification Bell subscribe like this
20:03
video If you guys haven't seen any of my
20:04
previous videos I highly recommend that
20:06
you do so and with that thought thank
20:08
you so much for watching I'll see you
20:10
guys next time peace out fellas
Video created on Runway Gen1 and later edited. Frames are created by merging images generated on Adobe Firefly. The prompt: “Imagine a visually engaging digital banner that captures the essence of the interplay between design, generative AI, and language models. Highlight the significance of semiotics and meta-design in shaping the future of these fields. Your poster should: Incorporate visually striking elements that represent the key concepts: semiotics, and design thinking. Use icons, colour, and layout creatively to convey the complex relationships between these concepts. Consider using symbolic imagery, and visual metaphors. “
The successful development of Orca illustrates the harmonious convergence of semiotics and technology, offering exciting opportunities for the application of design thinking methods in future LLM designs. Semiotic engineering, a discipline that considers interactive systems as meta-communication artefacts, emphasizes the importance of progressive semantization and the co-evolution of users and systems during usage. By integrating semiotic principles into the design process, future LLM designers can analyze the symbols, signs, and meaning behind user queries and responses, similar to how Orca interprets the reasoning process of GPT-4.
The rise of generative AI, particularly in the form of large language models (LLMs) like GPT, has sparked significant interest and concern within the design community. Designers are grappling with the implications of these technologies on creativity and society, leading to diverse reactions and questions about the future of design. However, rather than simply reacting to these advancements, designers have a crucial role to play in actively contributing to the development and improvement of AI systems. This article explores the integration of semiotics, design thinking, and meta-design as potential avenues for enhancing LLMs and fostering human-centred design in the context of generative AI.
I am excited by the release of a research paper by the @Microsoft research team just a few days ago titled ‘Orca: Progressive Learning from Complex Explanation Traces of GPT-4’. This paper unveils the Orca model, a powerful system comprised of 13 billion parameters, which acquires a profound understanding of explanation traces, sequential cognitive processes, and intricate directives extracted from GPT-4. Orca leverages GPT-4 to learn explanation traces, sequential cognitive processes, and intricate directives. Orca’s integration significantly enhances the performance of existing instruction-tuned models and offers intriguing possibilities for the incorporation of design methodologies and semiotic knowledge. By studying and emulating GPT-4’s reasoning processes, Orca demonstrates a profound understanding of semiotics, the study of signs and symbols, which underlies the communication between users and systems. For example, by analyzing the prompts “think step by step” and “explain it to me like I’m five,” Orca not only learns from the answers but also from the reasoning exhibited by GPT-4.
The successful development of Orca illustrates the harmonious convergence of semiotics and technology, offering exciting opportunities for the application of design thinking methods in future LLM designs. Semiotic engineering, a discipline that considers interactive systems as meta-communication artefacts, emphasizes the importance of progressive semantization and the co-evolution of users and systems during usage. By integrating semiotic principles into the design process, future LLM designers can analyze the symbols, signs, and meaning behind user queries and responses, similar to how Orca interprets the reasoning process of GPT-4.
Design thinking methods can also contribute to the evaluation and fine-tuning of LLMs. Orca’s extensive evaluation on multiple benchmarks showcases the power of rigorous assessment in measuring model performance. By incorporating design thinking principles, designers can develop comprehensive evaluation frameworks that go beyond simple question-answer pairs. This approach allows for detailed reasoning analysis and provides valuable insights into the strengths and weaknesses of the model, guiding iterative improvements and enabling LLMs to self-improve.
The conceptual framework of meta-design offers another perspective for designers to bridge the communication gap between users and designers and facilitate better user-system interaction. Meta-design enables domain experts to create software artefacts tailored to the end users’ culture, skills, and background, empowering users to act as designers during system usage. By incorporating semiotic engineering principles, designers can shape interactive systems that promote continuous user-system co-evolution and address the challenges posed by generative AI and language models [1]. The application of meta-design has already shown promising results in various domains, including medicine, cultural heritage, assistive technologies, e-government, neuro-rehabilitation, and robotics.
Created by merging images generated on Adobe Firefly. The prompt: “Imagine a visually engaging digital banner that captures the essence of the interplay between design, generative AI, and language models. Highlight the significance of semiotics and meta-design in shaping the future of these fields. Your poster should: Incorporate visually striking elements that represent the key concepts: semiotics, and design thinking. Use icons, colour, and layout creatively to convey the complex relationships between these concepts. Consider using symbolic imagery, and visual metaphors. “
The convergence of semiotics, design thinking, and meta-design presents a promising path forward for the design community in harnessing the potential of generative AI and LLMs. By incorporating these methodologies, designers can shape AI systems that align with societal needs, values, and user expectations. This holistic approach to design not only enhances the performance of LLMs but also fosters sustainable and human-centric AI platforms.
Our response to generative AI and LLMs should extend beyond reactive measures. Designers have a significant role to play in contributing to the development and improvement of AI systems. By integrating semiotics, design thinking, and meta-design, designers can enhance user-system communication, support user co-design, and enable LLMs to self-improve. This proactive approach holds promise for creating AI systems that are not only technologically advanced but also aligned with human needs and aspirations.
For a friendly video introduction to Orca: https://www.youtube.com/watch?v=Dt_UNg7Mchg
Further technical reaching : https://arxiv.org/pdf/2301.13688.pdf, https://arxiv.org/pdf/2305.17126.pdf, https://www.semianalysis.com/p/google-we-have-no-moat-and-neither,
Name
Published on 6/13/2023
In the rapidly evolving world of artificial intelligence, it's not always the biggest that make the most noise. Enter Orca 13B, a small yet mighty AI model developed by Microsoft that's making waves in the AI community. Despite its size, Orca 13B is proving that it can stand toe-to-toe with the giants, demonstrating capabilities that rival even the larger foundation models (LFMs) like ChatGPT and GPT-4.
This article delves into the fascinating world of Orca 13B, exploring its unique features, impressive performance, and the potential it holds for the future of AI. From its progressive learning approach to its remarkable performance in various benchmarks, we'll uncover how Orca 13B is redefining what's possible in AI. Whether you're an AI enthusiast, a researcher, or just curious about the latest developments in AI, this comprehensive guide to Orca 13B is sure to pique your interest.
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Orca 13B's progressive learning approach is a cornerstone of its success. By learning from rich signals from GPT-4, including explanation traces, step-by-step thought processes, and other complex instructions, Orca is able to develop a deeper understanding of the reasoning process. This is a significant departure from traditional AI models, which often focus on imitating the style of LFMs but fail to capture their reasoning process.
The use of explanation traces, for instance, allows Orca to understand the underlying logic behind the responses generated by GPT-4. This not only enhances Orca's ability to generate accurate responses, but also enables it to understand the context and nuances of different scenarios, thereby improving its overall performance.
Furthermore, the role of ChatGPT as a teacher assistant is crucial in providing a supportive learning environment for Orca. By providing guidance and feedback, ChatGPT helps Orca refine its learning process and improve its understanding of complex instructions. This teacher-student dynamic is a key factor in Orca's ability to imitate the reasoning process of LFMs.
Orca's performance in various benchmarks is a testament to its capabilities. In complex zero-shot reasoning benchmarks like Big-Bench Hard (BBH) and AGIEval, Orca surpasses conventional state-of-the-art instruction-tuned models such as Vicuna-13B by more than 100% and 42% respectively. This is a significant achievement, considering that these benchmarks are designed to test the model's ability to reason and make decisions in complex scenarios.
Moreover, Orca reaches parity with ChatGPT on the BBH benchmark, which is a remarkable feat given the size difference between the two models. This demonstrates Orca's ability to compete with larger models in terms of performance, despite its smaller size.
Orca also shows competitive performance in professional and academic examinations like the SAT, LSAT, GRE, and GMAT. This is particularly impressive as these examinations are designed to test a wide range of skills, including critical thinking, problem-solving, and analytical reasoning. The fact that Orca is able to perform at a competitive level in these examinations is a clear indication of its advanced capabilities.
You can learn more technical details from the original Orca 13B Paper
.
One of the most remarkable aspects of Orca is its size. Despite being a smaller AI model compared to giants like ChatGPT, Orca manages to perform at the same level. This is a significant breakthrough in technology as it demonstrates that powerful AI models can be built by smaller teams, making AI development more accessible.
The size of Orca also has implications for its efficiency and scalability. Being a smaller model, Orca requires less computational resources to train and operate, making it a more sustainable and cost-effective solution for AI development. Furthermore, its smaller size makes it easier to scale and adapt to different applications, thereby increasing its versatility and utility.
Microsoft's decision to open source Orca 13B in the coming months is a significant development in the AI community. This will allow users to dissect Orca, learn how to develop and train their own models, and even enhance Orca with their own input and ideas. The open sourcing of Orca is a reflection of Microsoft's commitment to AI and its belief in the potential of AI to transform technology.
By making Orca open source, Microsoft is not only promoting transparency and collaboration in the AI community, but also empowering individuals and smaller teams to contribute to the development of AI. This is a significant step towards democratizing AI and making it more accessible to a wider audience.
Furthermore, the open sourcing of Orca will provide valuable insights into the workings of a successful AI model. By studying Orca, users can gain a deeper understanding of the strategies and techniques used in its development, which can be applied to their own AI projects. This will not only enhance the quality of AI models developed by the community, but also accelerate the pace of innovation in the field of AI.
The open sourcing of Orca also presents an opportunity for users to enhance Orca with their own input and ideas. By allowing users to contribute to Orca's development, Microsoft is fostering a collaborative environment where the collective intelligence of the community can be harnessed to improve Orca and push the boundaries of what is possible in AI.
As we delve deeper into the capabilities of Orca 13B, it becomes clear that this AI model is not just a technological marvel, but also a tool with immense potential for practical applications. From academic research to business analytics, the possibilities are endless.
In the realm of academic research, Orca 13B can be a game-changer. Its ability to imitate the reasoning process of LFMs makes it an invaluable tool for researchers. For instance, in the field of social sciences, Orca can be used to analyze complex social phenomena and generate insightful explanations. Similarly, in the field of natural sciences, Orca can assist researchers in understanding complex natural processes by providing step-by-step explanations of these processes.
In the business world, Orca 13B can revolutionize the way companies analyze their data. By leveraging Orca's reasoning capabilities, businesses can gain deeper insights into their operations and make more informed decisions. For example, Orca can be used to analyze customer behavior patterns and provide detailed explanations of these patterns, enabling businesses to better understand their customers and tailor their services accordingly.
The future of AI looks promising with models like Orca 13B. By making Orca open source, Microsoft is not only promoting transparency and collaboration in the AI community, but also empowering individuals and smaller teams to contribute to the development of AI. This democratization of AI is a significant step towards harnessing the collective intelligence of the community to push the boundaries of AI.
As we continue to explore the potential of AI, models like Orca 13B will play a crucial role in shaping the future of this exciting field. Whether it's in academic research, business analytics, or any other field, the possibilities with Orca 13B are endless.
Orca 13B is a powerful AI model that demonstrates the potential of smaller models to rival the giants. Through its progressive learning approach, it has managed to imitate the reasoning process of LFMs, thereby enhancing its capabilities and skills. Its performance in various benchmarks is a testament to its capabilities, and its smaller size makes it a more accessible and sustainable solution for AI development.
The future of Orca looks promising, with Microsoft planning to open source the model in the coming months. This will not only provide valuable insights into the workings of a successful AI model, but also empower individuals and smaller teams to contribute to the development of AI. As we continue to explore the potential of AI, models like Orca 13B will play a crucial role in shaping the future of this exciting field.
Throughout this article, we've explored the capabilities and potential of Orca 13B. However, you may still have some questions. Here are some frequently asked questions about Orca 13B:
Orca 13B is a smaller AI model compared to giants like ChatGPT, yet it manages to perform at the same level. This is a significant breakthrough in technology as it demonstrates that powerful AI models can be built by smaller teams, making AI development more accessible.
Orca 13B learns from rich signals from GPT-4, including explanation traces, step-by-step thought processes, and other complex instructions. This learning process is guided by teacher assistance from ChatGPT, which provides a supportive learning environment for Orca.
Microsoft plans to open source Orca 13B in the coming months. This will allow users to dissect Orca, learn how to develop and train their own models, and even enhance Orca with their own input and ideas.
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A guide to improving your existing business application of artificial intelligence
מדריך לשיפור היישום העסקי הקיים שלך בינה מלאכותית
What is Artificial Intelligence and how does it work? What are the 3 types of AI? The 3 types of AI are: General AI: AI that can perform all of the intellectual tasks a human can. Currently, no form of AI can think abstractly or develop creative ideas in the same ways as humans. Narrow AI: Narrow AI commonly includes visual recognition and natural language processing (NLP) technologies. It is a powerful tool for completing routine jobs based on common knowledge, such as playing music on demand via a voice-enabled device. Broad AI: Broad AI typically relies on exclusive data sets associated with the business in question. It is generally considered the most useful AI category for a business. Business leaders will integrate a broad AI solution with a specific business process where enterprise-specific knowledge is required. How can artificial intelligence be used in business? AI is providing new ways for humans to engage with machines, transitioning personnel from pure digital experiences to human-like natural interactions. This is called cognitive engagement. AI is augmenting and improving how humans absorb and process information, often in real-time. This is called cognitive insights and knowledge management. Beyond process automation, AI is facilitating knowledge-intensive business decisions, mimicking complex human intelligence. This is called cognitive automation. What are the different artificial intelligence technologies in business? Machine learning, deep learning, robotics, computer vision, cognitive computing, artificial general intelligence, natural language processing, and knowledge reasoning are some of the most common business applications of AI. What is the difference between artificial intelligence and machine learning and deep learning? Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. What are the current and future capabilities of artificial intelligence? Current capabilities of AI include examples such as personal assistants (Siri, Alexa, Google Home), smart cars (Tesla), behavioral adaptation to improve the emotional intelligence of customer support representatives, using machine learning and predictive algorithms to improve the customer’s experience, transactional AI like that of Amazon, personalized content recommendations (Netflix), voice control, and learning thermostats. Future capabilities of AI might probably include fully autonomous cars, precision farming, future air traffic controllers, future classrooms with ambient informatics, urban systems, smart cities and so on. To know more about the scope of artificial intelligence in your business, please connect with our expert.
מהי בינה מלאכותית וכיצד היא פועלת? מהם 3 סוגי הבינה המלאכותית? שלושת סוגי הבינה המלאכותית הם: בינה מלאכותית כללית: בינה מלאכותית שיכולה לבצע את כל המשימות האינטלקטואליות שאדם יכול. נכון לעכשיו, שום צורה של AI לא יכולה לחשוב בצורה מופשטת או לפתח רעיונות יצירתיים באותן דרכים כמו בני אדם. בינה מלאכותית צרה: בינה מלאכותית צרה כוללת בדרך כלל טכנולוגיות זיהוי חזותי ועיבוד שפה טבעית (NLP). זהו כלי רב עוצמה להשלמת עבודות שגרתיות המבוססות על ידע נפוץ, כגון השמעת מוזיקה לפי דרישה באמצעות מכשיר התומך בקול. בינה מלאכותית רחבה: בינה מלאכותית רחבה מסתמכת בדרך כלל על מערכי נתונים בלעדיים הקשורים לעסק המדובר. זה נחשב בדרך כלל לקטגוריית הבינה המלאכותית השימושית ביותר עבור עסק. מנהיגים עסקיים ישלבו פתרון AI רחב עם תהליך עסקי ספציפי שבו נדרש ידע ספציפי לארגון. כיצד ניתן להשתמש בבינה מלאכותית בעסק? AI מספקת דרכים חדשות לבני אדם לעסוק במכונות, ומעבירה את הצוות מחוויות דיגיטליות טהורות לאינטראקציות טבעיות דמויות אדם. זה נקרא מעורבות קוגניטיבית. בינה מלאכותית מגדילה ומשפרת את האופן שבו בני אדם קולטים ומעבדים מידע, לעתים קרובות בזמן אמת. זה נקרא תובנות קוגניטיביות וניהול ידע. מעבר לאוטומציה של תהליכים, AI מאפשר החלטות עסקיות עתירות ידע, תוך חיקוי אינטליגנציה אנושית מורכבת. זה נקרא אוטומציה קוגניטיבית. מהן טכנולוגיות הבינה המלאכותית השונות בעסק? למידת מכונה, למידה עמוקה, רובוטיקה, ראייה ממוחשבת, מחשוב קוגניטיבי, בינה כללית מלאכותית, עיבוד שפה טבעית וחשיבת ידע הם חלק מהיישומים העסקיים הנפוצים ביותר של AI. מה ההבדל בין בינה מלאכותית ולמידת מכונה ולמידה עמוקה? בינה מלאכותית (AI) מיישמת ניתוח מתקדמות וטכניקות מבוססות לוגיקה, כולל למידת מכונה, כדי לפרש אירועים, לתמוך ולהפוך החלטות לאוטומטיות ולנקוט פעולות. למידת מכונה היא יישום של בינה מלאכותית (AI) המספק למערכות את היכולת ללמוד ולהשתפר מניסיון באופן אוטומטי מבלי להיות מתוכנתים במפורש. למידה עמוקה היא תת-קבוצה של למידת מכונה בבינה מלאכותית (AI) שיש לה רשתות המסוגלות ללמוד ללא פיקוח מנתונים שאינם מובנים או ללא תווית. מהן היכולות הנוכחיות והעתידיות של בינה מלאכותית? היכולות הנוכחיות של AI כוללות דוגמאות כמו עוזרים אישיים (Siri, Alexa, Google Home), מכוניות חכמות (Tesla), התאמה התנהגותית לשיפור האינטליגנציה הרגשית של נציגי תמיכת לקוחות, שימוש בלמידת מכונה ואלגוריתמים חזויים כדי לשפר את חווית הלקוח, עסקאות בינה מלאכותית כמו זו של אמזון, המלצות תוכן מותאמות אישית (Netflix), שליטה קולית ותרמוסטטים ללמידה. יכולות עתידיות של AI עשויות לכלול כנראה מכוניות אוטונומיות מלאות, חקלאות מדויקת, בקרי תעבורה אוויריים עתידיים, כיתות עתידיות עם אינפורמטיקה סביבתית, מערכות עירוניות, ערים חכמות וכן הלאה. כדי לדעת יותר על היקף הבינה המלאכותית בעסק שלך, אנא צור קשר עם המומחה שלנו.
Application Programming Interface(API):
An API, or application programming interface, is a set of rules and protocols that allows different software programs to communicate and exchange information with each other. It acts as a kind of intermediary, enabling different programs to interact and work together, even if they are not built using the same programming languages or technologies. API's provide a way for different software programs to talk to each other and share data, helping to create a more interconnected and seamless user experience.
Artificial Intelligence(AI):
the intelligence displayed by machines in performing tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and language understanding. AI is achieved by developing algorithms and systems that can process, analyze, and understand large amounts of data and make decisions based on that data.
Compute Unified Device Architecture(CUDA):
CUDA is a way that computers can work on really hard and big problems by breaking them down into smaller pieces and solving them all at the same time. It helps the computer work faster and better by using special parts inside it called GPUs. It's like when you have lots of friends help you do a puzzle - it goes much faster than if you try to do it all by yourself.
The term "CUDA" is a trademark of NVIDIA Corporation, which developed and popularized the technology.
Data Processing:
The process of preparing raw data for use in a machine learning model, including tasks such as cleaning, transforming, and normalizing the data.
Deep Learning(DL):
A subfield of machine learning that uses deep neural networks with many layers to learn complex patterns from data.
Feature Engineering:
The process of selecting and creating new features from the raw data that can be used to improve the performance of a machine learning model.
Freemium:
You might see the term "Freemium" used often on this site. It simply means that the specific tool that you're looking at has both free and paid options. Typically there is very minimal, but unlimited, usage of the tool at a free tier with more access and features introduced in paid tiers.
Generative Art:
Generative art is a form of art that is created using a computer program or algorithm to generate visual or audio output. It often involves the use of randomness or mathematical rules to create unique, unpredictable, and sometimes chaotic results.
Generative Pre-trained Transformer(GPT):
GPT stands for Generative Pretrained Transformer. It is a type of large language model developed by OpenAI.
GitHub:
GitHub is a platform for hosting and collaborating on software projects
Google Colab:
Google Colab is an online platform that allows users to share and run Python scripts in the cloud
Graphics Processing Unit(GPU):
A GPU, or graphics processing unit, is a special type of computer chip that is designed to handle the complex calculations needed to display images and video on a computer or other device. It's like the brain of your computer's graphics system, and it's really good at doing lots of math really fast. GPUs are used in many different types of devices, including computers, phones, and gaming consoles. They are especially useful for tasks that require a lot of processing power, like playing video games, rendering 3D graphics, or running machine learning algorithms.
Large Language Model(LLM):
A type of machine learning model that is trained on a very large amount of text data and is able to generate natural-sounding text.
Machine Learning(ML):
A method of teaching computers to learn from data, without being explicitly programmed.
Natural Language Processing(NLP):
A subfield of AI that focuses on teaching machines to understand, process, and generate human language
Neural Networks:
A type of machine learning algorithm modeled on the structure and function of the brain.
Neural Radiance Fields(NeRF):
Neural Radiance Fields are a type of deep learning model that can be used for a variety of tasks, including image generation, object detection, and segmentation. NeRFs are inspired by the idea of using a neural network to model the radiance of an image, which is a measure of the amount of light that is emitted or reflected by an object.
OpenAI:
OpenAI is a research institute focused on developing and promoting artificial intelligence technologies that are safe, transparent, and beneficial to society
Overfitting:
A common problem in machine learning, in which the model performs well on the training data but poorly on new, unseen data. It occurs when the model is too complex and has learned too many details from the training data, so it doesn't generalize well.
Prompt:
A prompt is a piece of text that is used to prime a large language model and guide its generation
Python:
Python is a popular, high-level programming language known for its simplicity, readability, and flexibility (many AI tools use it)
Reinforcement Learning:
A type of machine learning in which the model learns by trial and error, receiving rewards or punishments for its actions and adjusting its behavior accordingly.
Spatial Computing:
Spatial computing is the use of technology to add digital information and experiences to the physical world. This can include things like augmented reality, where digital information is added to what you see in the real world, or virtual reality, where you can fully immerse yourself in a digital environment. It has many different uses, such as in education, entertainment, and design, and can change how we interact with the world and with each other.
Stable Diffusion:
Stable Diffusion generates complex artistic images based on text prompts. It’s an open source image synthesis AI model available to everyone. Stable Diffusion can be installed locally using code found on GitHub or there are several online user interfaces that also leverage Stable Diffusion models.
Supervised Learning:
A type of machine learning in which the training data is labeled and the model is trained to make predictions based on the relationships between the input data and the corresponding labels.
Unsupervised Learning:
A type of machine learning in which the training data is not labeled, and the model is trained to find patterns and relationships in the data on its own.
Webhook:
A webhook is a way for one computer program to send a message or data to another program over the internet in real-time. It works by sending the message or data to a specific URL, which belongs to the other program. Webhooks are often used to automate processes and make it easier for different programs to communicate and work together. They are a useful tool for developers who want to build custom applications or create integrations between different software systems.
ממשק תכנות יישומים (API): API, או ממשק תכנות יישומים, הוא קבוצה של כללים ופרוטוקולים המאפשרים לתוכנות שונות לתקשר ולהחליף מידע ביניהן. הוא פועל כמעין מתווך, המאפשר לתוכניות שונות לקיים אינטראקציה ולעבוד יחד, גם אם הן אינן בנויות באמצעות אותן שפות תכנות או טכנולוגיות. ממשקי API מספקים דרך לתוכנות שונות לדבר ביניהן ולשתף נתונים, ועוזרות ליצור חווית משתמש מקושרת יותר וחלקה יותר. בינה מלאכותית (AI): האינטליגנציה שמוצגת על ידי מכונות בביצוע משימות הדורשות בדרך כלל אינטליגנציה אנושית, כגון למידה, פתרון בעיות, קבלת החלטות והבנת שפה. AI מושגת על ידי פיתוח אלגוריתמים ומערכות שיכולים לעבד, לנתח ולהבין כמויות גדולות של נתונים ולקבל החלטות על סמך הנתונים הללו. Compute Unified Device Architecture (CUDA): CUDA היא דרך שבה מחשבים יכולים לעבוד על בעיות קשות וגדולות באמת על ידי פירוקן לחתיכות קטנות יותר ופתרון כולן בו זמנית. זה עוזר למחשב לעבוד מהר יותר וטוב יותר על ידי שימוש בחלקים מיוחדים בתוכו הנקראים GPUs. זה כמו כשיש לך הרבה חברים שעוזרים לך לעשות פאזל - זה הולך הרבה יותר מהר מאשר אם אתה מנסה לעשות את זה לבד. המונח "CUDA" הוא סימן מסחרי של NVIDIA Corporation, אשר פיתחה והפכה את הטכנולוגיה לפופולרית. עיבוד נתונים: תהליך הכנת נתונים גולמיים לשימוש במודל למידת מכונה, כולל משימות כמו ניקוי, שינוי ונימול של הנתונים. למידה עמוקה (DL): תת-תחום של למידת מכונה המשתמש ברשתות עצביות עמוקות עם רבדים רבים כדי ללמוד דפוסים מורכבים מנתונים. הנדסת תכונות: תהליך הבחירה והיצירה של תכונות חדשות מהנתונים הגולמיים שניתן להשתמש בהם כדי לשפר את הביצועים של מודל למידת מכונה. Freemium: ייתכן שתראה את המונח "Freemium" בשימוש לעתים קרובות באתר זה. זה פשוט אומר שלכלי הספציפי שאתה מסתכל עליו יש אפשרויות חינמיות וגם בתשלום. בדרך כלל יש שימוש מינימלי מאוד, אך בלתי מוגבל, בכלי בשכבה חינמית עם יותר גישה ותכונות שהוצגו בשכבות בתשלום. אמנות גנרטיבית: אמנות גנרטיבית היא צורה של אמנות שנוצרת באמצעות תוכנת מחשב או אלגוריתם ליצירת פלט חזותי או אודיו. לרוב זה כרוך בשימוש באקראיות או בכללים מתמטיים כדי ליצור תוצאות ייחודיות, בלתי צפויות ולעיתים כאוטיות. Generative Pre-trained Transformer(GPT): GPT ראשי תיבות של Generative Pre-trained Transformer. זהו סוג של מודל שפה גדול שפותח על ידי OpenAI. GitHub: GitHub היא פלטפורמה לאירוח ושיתוף פעולה בפרויקטי תוכנה
Google Colab: Google Colab היא פלטפורמה מקוונת המאפשרת למשתמשים לשתף ולהריץ סקריפטים של Python בענן Graphics Processing Unit(GPU): GPU, או יחידת עיבוד גרפית, הוא סוג מיוחד של שבב מחשב שנועד להתמודד עם המורכבות חישובים הדרושים להצגת תמונות ווידאו במחשב או במכשיר אחר. זה כמו המוח של המערכת הגרפית של המחשב שלך, והוא ממש טוב לעשות הרבה מתמטיקה ממש מהר. GPUs משמשים סוגים רבים ושונים של מכשירים, כולל מחשבים, טלפונים וקונסולות משחקים. הם שימושיים במיוחד למשימות הדורשות כוח עיבוד רב, כמו משחקי וידאו, עיבוד גרפיקה תלת-ממדית או הפעלת אלגוריתמים של למידת מכונה. מודל שפה גדול (LLM): סוג של מודל למידת מכונה שאומן על כמות גדולה מאוד של נתוני טקסט ומסוגל ליצור טקסט בעל צליל טבעי. Machine Learning (ML): שיטה ללמד מחשבים ללמוד מנתונים, מבלי להיות מתוכנתים במפורש. עיבוד שפה טבעית (NLP): תת-תחום של AI המתמקד בהוראת מכונות להבין, לעבד וליצור שפה אנושית רשתות עצביות: סוג של אלגוריתם למידת מכונה המבוססת על המבנה והתפקוד של המוח. שדות קרינה עצביים (NeRF): שדות קרינה עצביים הם סוג של מודל למידה עמוקה שיכול לשמש למגוון משימות, כולל יצירת תמונה, זיהוי אובייקטים ופילוח. NeRFs שואבים השראה מהרעיון של שימוש ברשת עצבית למודל של זוהר תמונה, שהוא מדד לכמות האור שנפלט או מוחזר על ידי אובייקט. OpenAI: OpenAI הוא מכון מחקר המתמקד בפיתוח וקידום טכנולוגיות בינה מלאכותית שהן בטוחות, שקופות ומועילות לחברה. Overfitting: בעיה נפוצה בלמידת מכונה, שבה המודל מתפקד היטב בנתוני האימון אך גרועים בחדשים, בלתי נראים. נתונים. זה מתרחש כאשר המודל מורכב מדי ולמד יותר מדי פרטים מנתוני האימון, כך שהוא לא מכליל היטב. הנחיה: הנחיה היא פיסת טקסט המשמשת לתכנון מודל שפה גדול ולהנחות את הדור שלו Python: Python היא שפת תכנות פופולרית ברמה גבוהה הידועה בפשטות, בקריאות ובגמישות שלה (כלי AI רבים משתמשים בה) למידת חיזוק: סוג של למידת מכונה שבה המודל לומד על ידי ניסוי וטעייה, מקבל תגמולים או עונשים על מעשיו ומתאים את התנהגותו בהתאם. מחשוב מרחבי: מחשוב מרחבי הוא השימוש בטכנולוגיה כדי להוסיף מידע וחוויות דיגיטליות לעולם הפיזי. זה יכול לכלול דברים כמו מציאות רבודה, שבה מידע דיגיטלי מתווסף למה שאתה רואה בעולם האמיתי, או מציאות מדומה, שבה אתה יכול לשקוע במלואו בסביבה דיגיטלית. יש לו שימושים רבים ושונים, כמו בחינוך, בידור ועיצוב, והוא יכול לשנות את האופן שבו אנו מתקשרים עם העולם ואחד עם השני. דיפוזיה יציבה: דיפוזיה יציבה מייצרת תמונות אמנותיות מורכבות המבוססות על הנחיות טקסט. זהו מודל AI של סינתזת תמונות בקוד פתוח הזמין לכולם. ניתן להתקין את ה-Stable Diffusion באופן מקומי באמצעות קוד שנמצא ב-GitHub או שישנם מספר ממשקי משתמש מקוונים הממנפים גם מודלים של Stable Diffusion. למידה מפוקחת: סוג של למידת מכונה שבה נתוני האימון מסומנים והמודל מאומן לבצע תחזיות על סמך היחסים בין נתוני הקלט והתוויות המתאימות. למידה ללא פיקוח: סוג של למידת מכונה שבה נתוני האימון אינם מסומנים, והמודל מאומן למצוא דפוסים ויחסים בנתונים בעצמו. Webhook: Webhook הוא דרך של תוכנת מחשב אחת לשלוח הודעה או נתונים לתוכנית אחרת דרך האינטרנט בזמן אמת. זה עובד על ידי שליחת ההודעה או הנתונים לכתובת URL ספציפית, השייכת לתוכנית האחרת. Webhooks משמשים לעתים קרובות כדי להפוך תהליכים לאוטומטיים ולהקל על תוכניות שונות לתקשר ולעבוד יחד. הם כלי שימושי למפתחים שרוצים לבנות יישומים מותאמים אישית או ליצור אינטגרציות בין מערכות תוכנה שונות.
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