Hellooo, I’m looking to implement an OpenAI assistant using APIs but I want to do this locally on a group of files. I want to be able to train a GPT on the contents of a folder for example. Does anyone have any experience in this? It seems OpenAI needs a lot of uploading on each request if I were to do this with their API after playing around (but this feels like I’m missing something). It’s also quite costly to use. I was hoping to use local machine learning and models but this is quite limited in what it can do (eg Lumachain)

My App has several resources that I'd like to spring open through App Intents. For example a series of Dictionaries. These resources however in the app are behind a log in (for security) and are entitlements that are purchased. They may own 4 of 7 dictionaries. If I want to have an intent that says, "Open Dictionary: (Dict Name)" how do I best handle situations where the user may no longer be logged in or have the entitlement for that specific dictionary? Thanks

I have a Shortcuts action via an App Intent that I want only for active subscribers to use. I have a shared class that handles all the subcription related things. But for some reason my code only works if the app is active in the background. Once the app is quitted and the user performs the Shortcut, the not subscribed error is thrown – even though the user is subscribed. How can I ensure that my subscription check is done correctly, if the app isn’t open in the background? My Code App Intent excerpt: @MainActor func perform() async throws -> some IntentResult & ReturnsValue<MeterIntentEntity> { // Validate that the user is subscribed. // Cancels action with error message if not subscribed. if SubscriptionManager.shared.userIsSubscribed == false { throw IntentError.notSubscribed } // More Code … // Finish and pass created value as result. return .result(value: something) } Subscription Manager excerpt: class SubscriptionManager: ObservableObject { // A singleton for our entire app to use static let shared = SubscriptionManager() let productIds = ["my_sub1", "my_sub2"] @Published private(set) var availableSubscriptions: [Product] @Published private(set) var purchasedSubscriptions: [Product] = [] public var userIsSubscribed: Bool { return !self.purchasedSubscriptions.isEmpty } init() { // Initialize empty products, and then do a product request asynchronously to fill them in. availableSubscriptions = [] Task { await updatePurchasedProducts() } } @MainActor func updatePurchasedProducts() async { for await result in Transaction.currentEntitlements { do { let transaction = try checkVerified(result) if let subscription = availableSubscriptions.first(where: { $0.id == transaction.productID }) { purchasedSubscriptions.append(subscription) } } catch { Logger.subscription.error("Error loading users user's purchased products.") } } }

I'm trying to convert a TensorFlow model that I didn't create and know approximately nothing about to CoreML so that I can use it in some functional tests. I can't tell you much about the model, but you can read about it on the blog from the team that created it: https://research.google/blog/improving-mobile-app-accessibility-with-icon-detection/ I can't convert this model to a TensorFlow Lite model because it uses a few full TensorFlow operations (which I could work around) and it exceeds the 4-tensor output limit (which I can't, AFAIK). So instead, I'm trying to convert the model to CoreML so that I can run it on-device. The issue I'm running into is that every approach fails in different ways. If I load the model with tf.saved_model.load and pass that as the first parameter to the convert call, it says NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got <tensorflow.python.trackable.autotrackable.AutoTrackable object at 0x30d90c250> If I pass model.signatures['serving_default'] as the first parameter to convert, I get NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got ConcreteFunction [...a page or two of info about the function here...] If I try to wrap it in a Keras layer using the instructions provided in the converter, it fails because a sequential model can't have multiple outputs. If I try to use a tf.keras.layers.TFSMLayer to load the model, it fails because there are multiple tags, and there's no way to specify tags when constructing the layer. (It tells me that I need to add 'tags' to load the model, but if I do that, it tells me that tags isn't a valid parameter to the call.) If I load the model with tf.saved_model.load and specify a single tag, then re-save it in a different location with tf.saved_model.save to generate a new model with only a single tag, then do input_layer = tf.keras.Input(shape=(768, 768, 3), dtype="int8") layer = tf.keras.layers.TFSMLayer("./serve_model", call_endpoint='serving_default') outputs = layer(input_layer) model = tf.keras.Model(input_layer, outputs) I get AttributeError: 'Functional' object has no attribute '_get_save_spec' At one point, I also tried this: class LayerFromSavedModel(tf.keras.layers.Layer): def __init__(self): super(LayerFromSavedModel, self).__init__() self.vars = legacy_model.variables def call(self, inputs): return legacy_model.signatures['serving_default'](inputs) input = tf.keras.Input(shape=(3000, 3000, 3)) model = tf.keras.Model(input, LayerFromSavedModel()(input)) and saw a similar failure. I've run out of ideas here. Is there simply no support whatsoever in the converter for importing a TensorFlow 2 SavedModel into CoreML, or am I missing something fundamental?

We are currently working on implementing a baby cry detection model in the frontend of our app but have encountered some challenges with the mel spectrogram transformation. Our mel spectrogram class, developed in python, leverages librosa for generating mel spectrograms (librosa.feature.melspectrogram and librosa.power_to_db). While we have successfully exported the model to a .mlmodel file, the results we obtain in Swift differ significantly from those generated by our Python code. Could this discrepancy be due to the use of librosa in Python, which might not be directly compatible with Swift? Or should the transformation process be inherently consistent once exported to a .mlmodel file?

Hi, this is the 3rd time I'm trying to post this on the forum, apple moderators ignoring it. I'm a deep learning expert with a specialization of image processing. I want to know why I have hundreds of AI models on my Mac that are indexing everything on my computer while it is idle, using programs like neuralhash that I can't find any information about. I can understand if they are being used to enhance the user experience on Spotlight, Siri, Photos, and other applications, but I couldn't find the necessary information on the web. Usually, (spyware) software like this uses them to classify files in an X/Y coordinate system. This feels like a more advanced version of stuxnet. find / -type f -name "*.weights" > ai_models.txt find / -type f -name "*labels*.txt" > ai_model_labels.txt Some of the classes from the files; file_name: SCL_v0.3.1_9c7zcipfrc_558001-labels-v3.txt document_boarding_pass document_check_or_checkbook document_currency_or_bill document_driving_license document_office_badge document_passport document_receipt document_social_security_number hier_curation hier_document hier_negative curation_meme file_name: SceneNet5_detection_labels-v8d.txt CVML_UNKNOWN_999999 aircraft automobile bicycle bird bottle bus canine consumer_electronics feline fruit furniture headgear kite fish computer_monitor motorcycle musical_instrument document people food sign watersport train ungulates watercraft flower appliance sports_equipment tool

I was wondering if there is a quick way to convert a model trained with the open source CRFSuite for use with NLTagger? It seems like retraining should be possible but was wondering if automatic conversion was supported?

I am developing an iOS app that supports INPlayMediaIntent. We are trying to increase the recognition rate of content names, which are song titles, using AppIntentVocabulary. As a sample, some extracts are shown below. <?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> <plist version="1.0"> <dict> <key>IntentPhrases</key> <array> <dict> <key>IntentName</key> <string>INPlayMediaIntent</string> <key>IntentExamples</key> <array> <string>Mezamashi Appで湖畔の朝を再生</string> <string>湖畔の朝をMezamashi Appで再生して</string> </array> </dict> </array> <key>ParameterVocabularies</key> <array> <dict> <key>ParameterNames</key> <array> <string>INPlayMediaIntent.playlistTitle</string> </array> <key>ParameterVocabulary</key> <array> <dict> <key>VocabularyItemIdentifier</key> <string>ID1</string> <key>VocabularyItemSynonyms</key> <array> <dict> <key>VocabularyItemPronunciation</key> <string>aogamagaeru</string> <key>VocabularyItemPhrase</key> <string>青ガマガエル</string> </dict> </array> </dict> <dict> <key>VocabularyItemIdentifier</key> <string>ID2</string> <key>VocabularyItemSynonyms</key> <array> <dict> <key>VocabularyItemPronunciation</key> <string>kohon no asa</string> <key>VocabularyItemPhrase</key> <string>湖畔の朝</string> </dict> </array> </dict> <dict> <key>VocabularyItemIdentifier</key> <string>ID3</string> <key>VocabularyItemSynonyms</key> <array> <dict> <key>VocabularyItemPronunciation</key> <string>kumageratachi no uta</string> <key>VocabularyItemPhrase</key> <string>クマゲラたちの歌</string> </dict> </array> </dict> </array> </dict> </array> </dict> </plist> When running on the iOS 17.5 simulator in Xcode 15.4, the results are as follows. mediaName = VocabularyItemIdentifier mediaIdentifier = nil <INMediaSearch: 0x6000026212c0> { reference = 0; mediaType = 0; sortOrder = 0; albumName = <null>; mediaName = ID1; genreNames = ( ); artistName = <null>; moodNames = ( ); releaseDate = <null>; mediaIdentifier = <null>; } However, when running on an iOS 17.5 device, the following applies. mediaName = VocabularyItemPhrase mediaIdentifier = VocabularyItemIdentifier <INMediaSearch: 0x301efd9e0> { reference = 0; mediaType = 5; sortOrder = 0; albumName = <null>; mediaName = 青ガマガエル; genreNames = ( ); artistName = <null>; moodNames = ( ); releaseDate = <null>; mediaIdentifier = ID1; } The results are not stable, for example, sometimes everything else returns null. I have tried everything, but it is just taking a long time. Does anyone have any advice on this?

iOS 18 adds a specific macro for exposing your search app intent, app entities, etc, to siri but how are you meant to add it to your existing objects without removing it entirely from < iOS 18 users? For example, i get the following error: AssistantIntent(schema:) is only available in iOS 18 or newer. Add @available attribute to enclosing struct. I don't want to do that since i still want to support iOS 17 users with my existing shortcuts. Do i need to duplicate my entire shortcuts model to add the new macro?

The What’s New in Create ML session in WWDC24 went into great depth with time-series forecasting models (beginning at: 15:14) and mentioned these new models, capabilities, and tools for iOS 18. So, far, all I can find is API documentation. I don’t see any other session in WWDC24 covering these new time-series forecasting Create ML features. Is there more substance/documentation on how to use these with Create ML? Maybe I am looking in the wrong place but I am fairly new with ML. Are there any food truck / donut shop demo/sample code like in the video? It is of great interest to get ahead of the curve on this within business applications that may take advantage of this with inventory / ordering data.

I want to add shortcut and Siri support using the new AppIntents framework. Running my intent using shortcuts or from spotlight works fine, as the touch based UI for the disambiguation is shown. However, when I ask Siri to perform this action, she gets into a loop of asking me the question to set the parameter. My AppIntent is implemented as following: struct StartSessionIntent: AppIntent { static var title: LocalizedStringResource = "start_recording" @Parameter(title: "activity", requestValueDialog: IntentDialog("which_activity")) var activity: ActivityEntity @MainActor func perform() async throws -> some IntentResult & ProvidesDialog { let activityToSelect: ActivityEntity = self.activity guard let selectedActivity = Activity[activityToSelect.name] else { return .result(dialog: "activity_not_found") } ... return .result(dialog: "recording_started \(selectedActivity.name.localized())") } } The ActivityEntity is implemented like this: struct ActivityEntity: AppEntity { static var typeDisplayRepresentation = TypeDisplayRepresentation(name: "activity") typealias DefaultQuery = MobilityActivityQuery static var defaultQuery: MobilityActivityQuery = MobilityActivityQuery() var id: String var name: String var icon: String var displayRepresentation: DisplayRepresentation { DisplayRepresentation(title: "\(self.name.localized())", image: .init(systemName: self.icon)) } } struct MobilityActivityQuery: EntityQuery { func entities(for identifiers: [String]) async throws -> [ActivityEntity] { Activity.all()?.compactMap({ activity in identifiers.contains(where: { $0 == activity.name }) ? ActivityEntity(id: activity.name, name: activity.name, icon: activity.icon) : nil }) ?? [] } func suggestedEntities() async throws -> [ActivityEntity] { Activity.all()?.compactMap({ activity in ActivityEntity(id: activity.name, name: activity.name, icon: activity.icon) }) ?? [] } } Has anyone an idea what might be causing this and how I can fix this behavior? Thanks in advance

Are there going to be any sessions on Image Playgrounds API for iOS? "Explore machine learning on Apple platforms" mentions the writing and points to sessions, but only mentions Image Playground without pointing to sessions.

I am trying to make a voip car play app using siri let assistant = CPAssistantCellConfiguration(position: .top, visibility: .always, assistantAction: .startCall) let siriTmeplate = CPListTemplate(title: "Siri", sections: [sectionItems, loadingSection], assistantCellConfiguration: assistant) siriTmeplate.tabSystemItem = .recents siriTmeplate.showsTabBadge = false Using the above code gives me the error "Error: Intent of type INStartCallIntent is not supported for this app category" on app luanch I have INStartCallIntent in my apps info plist and I have all the entitlements and I have "business" as the app category, I can fine 0 help online with this. what does this error really mean and how can I fix it please

Hi, I have a custom object detection CoreML model and I notice something strange when using the model with the Vision framework. I have tried two different approaches as to how to process an image and do inference on the CoreML model. The first one is using the CoreML "raw": initialising the model, getting the input image ready and using the model's .prediction() function to get the models output. The second one is using Vision to wrap the CoreML model in a VNCoreMLModel, creating a VNCoreMLRequest and using the VNImageRequestHandler to actually perform the model inference. The result of the VNCoreMLRequest is of type VNRecognizedObjectObservation. The issue I now face is in the difference in the output of both methods. The first method gives back the raw output of the CoreML model: confidence and coordinates. The confidence is an array with size equal to the number of classes in my model (3 in my case). The second method gives back the boundingBox, confidence and labels. However here the confidence is only the confidence for the most likely class (so size is equal to 1). But the confidence I get from the second approach is quite different from the confidence I get during the first approach. I can use either one of the approaches in my application. However, I really want to find out what is going on and understand how this difference occurred. Thanks!

I've been going through the documentation. I can't seem to find the docs that cover all the new AI features.

From https://www.apple.com/newsroom/2024/06/introducing-apple-intelligence-for-iphone-ipad-and-mac/: Powered by Apple Intelligence, Siri becomes more deeply integrated into the system experience. With richer language-understanding capabilities, Siri is more natural, more contextually relevant, and more personal, with the ability to simplify and accelerate everyday tasks. From https://developer.apple.com/apple-intelligence/: Siri is more natural, more personal, and more deeply integrated into the system. Apple Intelligence provides Siri with enhanced action capabilities, and developers can take advantage of pre-defined and pre-trained App Intents across a range of domains to not only give Siri the ability to take actions in your app, but to make your app’s actions more discoverable in places like Spotlight, the Shortcuts app, Control Center, and more. SiriKit adopters will benefit from Siri’s enhanced conversational capabilities with no additional work. And with App Entities, Siri can understand content from your app and provide users with information from your app from anywhere in the system. Based on this, as well as the video at https://developer.apple.com/videos/play/wwdc2024/10133/ , my understanding is that in order for Siri to be able to execute tasks in applications, those applications must implement the Siri Intents API. Can someone at Apple please clarify: will it be possible for Siri or some other aspect of Apple Intelligence / Core ML / Create ML to take actions in applications which do not support these APIs (e.g. web apps, Citrix apps, legacy apps)? Thank you!

I'm using Filemaker, with Monkey Bread Software plugin's CoreML features, to find that it can only write to .mlmodelc. Are these (.mlmodel = .mlmodelc) the same? If not, how do you generate a .mlmodelc using XCode. Please let me know, thanks.

I have an mlprogram of size 127.2MB it was created using tensorflow and then converted to CoreML. When I request a prediction the amount of memory shoots up to 2-2.5GB every time. I've tried using the optimization techniques in coremltools but nothing seems to work it still shoots up to the same 2-2.5GB of ram every time. I've attached a graph to see it doesn't seem to be a leak as the memory is then going back down.

Hi all, I couldn't use random.PRNGKey to generate random seed. Wondering anyone has similar issue before and figure this out. Here is my current config: jax-metal==0.0.3, jaxlib==0.4.10, jax==0.4.11. I am using Apple M1 pro.

I made a model using pytorch and then converted it into a mlmodel file. Next I tried and downloaded (https://developer.apple.com/documentation/vision/recognizing_objects_in_live_capture) which worked! But when I changed the model to my model that I made, the camera worked, but no predictions where shown please h elp!