Microsoft Launches BioGPT, the ChatGPT of Life Science
Microsoft has unveiled BioGPT, a new artificial intelligence (AI) tool that can be used to assess biomedical research in order to provide biomedical queries with answers. This technology is particularly pertinent for assisting researchers in gaining fresh perspectives. BioGPT is trained using millions of previously published biomedical research articles
Spotify Launches AI-Powered ‘DJ’ Feature Using OpenAI Technology
A computerised song-spinner with a “stunningly realistic” voice that queues up music based on your musical preferences and listening history is now a feature of Spotify’s app. Starting on Wednesday, English-speaking Spotify Premium subscribers in the U.S. and Canada will be able to access the DJ beta version.
Instagram co-founders’ new AI-powered news app ‘Artifact’ now open to all
Instagram co-founders Kevin Systrom and Mike Krieger have released Artifact, a new artificial intelligence (AI)-driven personalised news feed application, along with new features. The Artifact team announced in a blog post on Wednesday that anyone can now download and use the new programme without a waitlist or phone number.
Comic book loses copyright of AI-created images in US
According to a letter from the US Copyright Office seen by Reuters, images in a graphic novel that were produced by the artificial intelligence programme Midjourney shouldn’t have been given copyright protection. Kris Kashtanova, the author of “Zarya of the Dawn,” is granted a copyright for the portions of the book she wrote and organised, but not for the pictures created by Midjourney, according to the office.
Microsoft brings AI-powered Bing to mobile and Skype
Microsoft is now delivering the AI-based Bing chatbot’s functionality to smartphones and Skype, two weeks after its debut. The Bing button in the app would allow users who have been invited to the preview to initiate a conversation with the chatbot. Users of Skype on all platforms can have private conversations with Bing or add it to group chats where anyone can tag it and ask a question.
The possibility of cloud computing offers organizations great potential for streamlining operations and improving productivity. And given the current landscape of remote working and globalized operations, who wouldn’t want to gain access to their data and applications no matter their geographical location or the device they’re using?
But what if an organization has sensitive data or specific workflows that need to be kept separate? Is it possible to safely store critical assets in the cloud while still keeping them secure? The answer is yes – thanks to the concept of isolation.
What Is Isolation In Cloud Computing?
Isolation in the cloud refers to a set-up where certain elements (such as virtual machines or containers) are kept separated from other parts within an overall cloud environment. This is achieved by taking advantage of features like firewalls, networks, routing tables, and VPCs (Virtual Private Clouds).
By implementing these features correctly, organizations can create multiple isolated environments within one shared infrastructure that helps protect their data and applications from malicious actors on the public web.
Isolation can be used on public clouds like Amazon and Microsoft Azure, as well as private enterprise cloud systems that leverage open-source tools like OpenStack or CloudStack.For the latter option, though, depending on what security level is necessary for your system, physical hardware segregation might also be necessary.
How Can Isolation Help Protect Organizations from Threats?
One of the main benefits of isolating applications and data in a shared environment is improved security since each part is effectively locked away from prying eyes. This means that even if attackers gain access to one virtual machine or container, they will not be able to penetrate other areas since they lack network connectivity with them.
As a result, only authorized people will have access to sensitive information stored in isolated parts – significantly lowering the risk of an external attack leading to vulnerabilities being exploited elsewhere in an organization’s IT landscape.
Additionally, since resources remain within an organization’s private network, there won’t be any need for costly additional infrastructure investments such as global servers or redundant backups, thus helping reduce operational costs over time.
Furthermore, unlike traditional IT setups where tasks like scaling or patching would require expensive manual labor, isolating components makes automating these processes much simpler and more efficient, allowing businesses to focus their effort on improving customer service rather than struggling with tedious maintenance issues.
What Are The Benefits Of Isolating Applications In The Cloud?
In addition to improved security and cost savings mentioned above, several other key advantages come with isolating applications in a shared cloud setup:
Reduced complexity
Applications designed around isolated architectures have fewer elements which makes them easier to maintain over time while cutting down on time wasted dealing with complicated scripting errors across different systems/servers. This helps organizations focus on developing their core applications and services.
Greater flexibility
Virtualization technology allows organizations to scale up (or down) resources quickly based on changing demand patterns, thus making it possible to accommodate fluctuations easily during peak periods. The adaptability of virtualized applications also makes it simple to add new features and modifications; without needing to reconfigure the application’s whole infrastructure.
Improved performance
The fact that less traffic needs to transfer between different components results in increased performance across apps being supported by isolated infrastructures. For example, Isolated infrastructures can also help address issues with BigQuery latency, as fewer network hops are required to transfer data between different components within the cloud environment.
Better reliability
By breaking up complex processes into smaller chunks housed on separate networks, organizations dramatically decrease their chances of becoming disrupted due to massive system failures. This increased dependability is particularly beneficial for mission-critical services or applications, as well as those that must adhere to industry standards or regulations.
Common Challenges Faced When Deploying Isolated Infrastructures
Although there are many benefits associated with using isolated clouds, organizations must also bear certain factors in mind when first designing their architecture:
Infrastructure cost: Physical hardware requirements for running multiple instances may increase costs significantly.
Limited scalability: Since each part is only connected locally rather than globally – adding new users may not always be easy.
Security risks: Even though isolation can help protect against external attacks, extreme caution should be taken when granting user permissions (especially ones accessing sensitive information) inside any given environment.
The Takeaway
For those looking to take advantage of all the goodness offered by today’s cloud computing technologies but want to maintain maximum control over critical workloads, understanding how to isolate applications is an extremely important step towards doing so securely and efficiently.
By following best practices recommended by industry experts combined with careful planning ahead of the stage, organizations can position themselves to reap the full benefits of deployed solutions without having to suffer the dreaded consequences of poor planning and unpreparedness. Just remember that in the cloud, one size doesn’t fit all, so take some time to weigh all options before committing!
1. Infants Outperform AI in “Commonsense Psychology ”
According to a recent study by a group of psychology and data science academics from NYU, infants are better than artificial intelligence at determining what drives other people’s behaviour. The results point out basic disparities between computer and human cognition, highlighting flaws in the state-of-the-art machine learning, and pinpointing areas in which advancements are required for AI to accurately mimic human behaviour.
2. Amazon Web Services pairs with Hugging Face to target AI developers
The cloud computing division of Amazon, Amazon Web Services (AWS), announced on Tuesday that it is working with startup Hugging Face, a software development hub, to make it simpler to execute artificial intelligence (AI) tasks on Amazon’s cloud.
3. Microsoft’s Bing AI Bot is increasing its Chat Session limit
The current five chat turns per session and 50 per day will be increased to six chat turns per session and 60 total chats per day for Microsoft Bing, with a 100-total chat limit soon after, This is Microsoft’s first step towards extending conversation restrictions.
4. NVIDIA GTC 2023 to Feature Latest Advances in AI
Jensen Huang, the founder and CEO of NVIDIA, will give the opening keynote address at GTC 2023. He will discuss the most recent developments in generative AI, the metaverse, massive language models, robotics, cloud computing, and other areas. The four-day event, which will have 650+ sessions from researchers, developers, and industry leaders in essentially every computing sector, will take place from March 20-23.
5. India ranks first in AI skill penetration and talent concentration: NASSCOM report
India ranks first in AI skill penetration and talent concentration, and sixth in AI scientific publications, according to a new NASSCOM report. According to the report, Indian tech talent is three times more likely than that of other nations to possess or report AI skills. This shows that Indian IT talent is quickly becoming proficient in DS&AI.
Learning about a topic such as machine learning operations requires some work. Here, we will take a closer look at the key concepts and the workflow of MLOps and give you a more thorough understanding of the subject matter.
What Is MLOps?
Think of MLOps as the bridge between the worlds of data science and operations. On one side, data scientists are focused on developing and fine-tuning machine learning models, using advanced algorithms and techniques to extract insights from data.
On the other side, operations teams are focused on ensuring that systems are secure, reliable, and scalable, and that data is properly managed and protected. MLOps is what brings these two worlds together, providing a set of best practices and tools that enable data scientists and operations teams to work together seamlessly and effectively.
At its core, MLOps is all about making machine learning more accessible and more manageable so that organizations can deploy models more quickly and with greater confidence and ensure that they continue to perform well over time. Whether you’re working in a large enterprise or a small startup, MLOps is a critical component of any successful machine learning initiative.
Key Concepts of MLOps
These concepts help to ensure the reliability, security, and scalability of machine learning models in production and support the effective deployment and management of machine learning systems:
Continuous Integration and Continuous Deployment (CI/CD): Imagine a machine learning model like a house. CI/CD is like having an assembly line that automatically builds and tests each room of the house before it is added to the main structure, ensuring a high-quality finished product.
Version Control: Picture each version of a machine learning model like a snapshot in time. Version control helps you keep track of all these snapshots, so you can compare and revert to previous versions if needed, much like flipping through a family photo album.
Monitoring and Metrics: Monitoring and metrics are like the speedometer and gas gauge of a car. They provide real-time feedback on how the model is performing, so you can make adjustments and fine-tune it to keep it running smoothly.
Model Management: Model management is like having a filing cabinet that keeps all your important papers organized and in one place. It helps you keep track of all your machine learning models, so you can quickly find what you need and make informed decisions.
Data Management: Data management is like having a carefully tended garden. You need to plant the seeds, water the plants, and make sure they’re protected from pests to ensure a bountiful harvest. In the same way, you need to manage your data to ensure it’s high quality and protected.
Collaboration: Collaboration is like having a team of builders working together to construct a house. By working together and sharing information, you can ensure that each part of the machine learning project is completed on time and to the highest quality.
By focusing on these concepts, MLOps helps organizations bring their machine learning initiatives to life and ensure they continue to deliver value over time.
MLOps Workflow
Let’s discuss the entire MLOps workflow. We’re going to provide each step of the workflow and try to discuss some examples that might help you understand what is really done during the mentioned step.
Image Credit: Unsplash
Model Development
The first step of the MLOps workflow is the development of the model. Data scientists use algorithms and frameworks to develop ML models. An example of this step would be a data scientist training a deep learning model to identify objects in images.
Model Testing
As everything should be, the models are tested as well. The workflow involves a thorough evaluation of the accuracy, robustness, and performance. It’s simply necessary to continue the process.
A data scientist uses a validation dataset to evaluate the model’s accuracy and performance. We test the model for various parameters such as precision, recall, and F1 score. The F1 score is a measure of a test’s accuracy that balances precision and recall.
Version Control
The third step involves versioning the models. So, the models and all related artifacts are versioned and stored in a central repository. This is simple version control. Data scientists store the model and the code related to it in a Git repository.
Continuous Integration
The fourth step is continuous integration. We integrate the models with the CI/CD pipeline, and we test them to verify their functionality. This is where we go back to the testing.
Before moving to the next step, the data scientists set up a CI/CD pipeline using tools such as Jenkins. The pipeline automatically runs tests on the model once we update it in the Git repository.
Continuous Deployment
Continuous deployment means that we deploy successful models to a staging environment for further testing. It’s a key component of continuous delivery where code changes are automatically built, tested, and deployed to production.
So, if a model passes the tests, it is automatically deployed to a staging environment for further testing. In the staging environment, the model is tested in a simulated production environment. This involves further monitoring.
Model Monitoring
This step involves detailed monitoring of the performance and the behavior of the models in the production environment. We monitor the deployed model using tools such as Prometheus and Grafana. They track metrics such as model accuracy and response time.
Prometheus is a time-series database and monitoring system that collects and stores metrics. Grafana is a dashboard and visualization platform that allows users to create, explore, and share interactive dashboards based on data stored in Prometheus.
Model Optimization
The seventh step is model optimization, which means fine-tuning the models based on the monitoring results from the previous step. If necessary, they are updated.
Based on the results of monitoring, data scientists fine-tune the model to improve its performance. The model is then updated and redeployed to the production environment. This ensures smoother operation.
Model Rollback
If the model that gets updated causes certain issues in production, the data scientists can roll back the model to its previous version using Git tags. This is a vital step of the workflow because we can address certain issues once we roll back.
Model Retirement
When models are no longer needed, they can be retired and removed from the production environment. We need to do this to preserve resources. The model can be archived in the Git repository for future reference.
Conclusion
It’s important to remember that MLOps works through CI/CD, version control, monitoring and metrics, model management, data management, and collaboration. Without these concepts, there would be no MLOps.
Businesses spend a lot of money creating content marketing campaigns in this cutthroat environment.
Content marketing is an important and effective growth strategy for many organizations because it is one of the best ways to increase engagement, boost sales, and establish your brand’s visibility. Here are five excellent reasons to consider content marketing for your brand, explained by a pro paper writer from the best on the market custom writing service for college students and business owners.
1. It significantly increases trust
After reading some of the business’s informative content, most customers feel the brand is both positive and trustworthy.
Such content is an excellent trust-builder since it targets specific audiences and addresses their queries and problems. After all, a fundamental tenet of content marketing is conducting sufficient research on your potential clients to achieve everything said above.
Helping your audience regularly with relevant, customized material demonstrates the brand’s competence and empathy.
Finally, empathy shows prospective customers that you are worried about them and that you understand their difficulties. The icing on the cake is that you also offer them solutions in the form of your services and goods.
2. It is cost-effective
The cost of content marketing is relatively low. And according to a DemandMetric analysis, using content as a way to attract customers gets you almost three times as many leads while being about 62% cheaper than traditional marketing strategies.
Any small firm that wants to expand should be privy to this information. Compared to many conventional marketing strategies, the one involving content is both more affordable and successful.
You should understand that content marketing requires time, and results can take a while to manifest. The same is true about SEO. However, a little can go a long way in producing high-quality content, especially if you get professional help. You can hire an experienced essay writing service EssayHub.com, to write content for you.
Remember that writing quality content requires time and work unless you employ the best essay writers to pay for essay. The search engine crawlers require time to ingest and rank the information after it has been uploaded. The ranks for excellent content will improve over time. It does not occur overnight.
The secret to a successful content marketing plan is having a thorough understanding of your target audience, what they enjoy reading, and where they prefer to consume content.
Creating a profile of the perfect customer, or client “Persona,” is a crucial first step in a successful content strategy. When you have a better understanding of your audience, you can develop content that specifically addresses the issues they are researching online.
3. It helps generate higher-quality leads
High-quality leads are those with an excellent possibility of becoming consumers and are interested in your brand and content.
They recognize your brand and know your area of specialization and the products you offer. They are also ready to make a purchase, or they are aware of their issue, are aware of the best solution, and are prepared to locate it.
Content and search engine optimization work excellently together. Your content will attract leads by targeting keywords with the appropriate search intent that are looking for the solutions or answers you offer.
People employ specific phrases or words in their searches that provide significant hints on how close (or distant) they are from completing a purchase.
4. It helps achieve a profound understanding of your target audience
You need to understand your audience completely to visualize who you are speaking to with the content you produce.
You will gradually better understand how your ideal customers interact with your content and take advantage of your offers by collecting analytics and conversion data related to your content marketing.
Your content performs better as your comprehension grows. Data is also a terrific tool to see how customers are using your website and how you may make changes to improve that trip.
5. It boosts your SEO efforts
When we consider how content marketing advances and builds search engine optimization for your company, its significance becomes more apparent. To increase your company’s online presence, SEO is crucial.
However, you must provide optimized content if you want to increase SEO. Pro essay writers or the best paper writing services can help you with this task as well. Compared to sites that don’t publish, brands that consistently publish blog content have significantly more pages indexed by search engines. The search engine must index and display to users more pages as a result of the increased amount of content on your website.
Although having many pages doesn’t always translate into higher traffic, it allows your company to rank for more keywords. For instance, writing blogs on various pertinent subjects grows your chances of appearing in search results for relevant terms.
The more reasons you provide visitors to stay on your site, the more material you should have. This entails spending more time on-site, which may also benefit your search engine optimization.
Conclusion
Do you want to offer your visitors valuable content continually? Launch an aggressive content marketing campaign. Consider using the best essay writing service to improve your content marketing strategy.
The usage of AI-powered ChatGPT for the class 10 and 12 board exams has been banned by the Central Board of Secondary Education (CBSE), and students who are found using the platform will be reported for using unfair means.
When students received their board exam admit cards this year, this was a surprising addition to the list of items prohibited inside the examination centre. CBSE has listed ChatGPT among mobile and other electronic items forbidden inside the examination centre.
A class 10 student parent, Harish Modak, said, “After I saw this word in the instructions list, I asked my son about it. He was equally unaware about it. Then I had to Google it and read about it. I do not think it could be used by any student during exams as it requires a device and CBSE has already mentioned a big list which is banned to carry in the exam hall.”
“The school has taken cognizance of the matter and instructed students about the same. It is a very welcoming decision by the Board, because at present our exam system is to test the ability of students to understand the concepts and its applications. It is the right step by the Board is my personal opinion,” Dr Sr. Kripa, principal, Carmel Convent, BHEL.
Recently, a Bengaluru-based RV University banned the use of OpenAI’s ChatGPT. The university banned the chatbot inside the campus to prohibit students from using it during the exams, assignments, and lab tests.
Microsoft Bing plans AI ads with advertising agencies
In an effort to challenge Google’s dominance, Microsoft has started talking to advertising agencies about how it intends to monetize its updated Bing search engine, which is powered by generative AI. According to an advertising executive, Microsoft is already testing ads in an early version of the Bing chatbot, which is accessible to only a small number of people.
The researchers at DAIICT create AI models for translating works
The Dhirubhai Ambani Institute of Information and Communication Technology (DAIICT) in Gandhinagar has researchers that have successfully developed AI models for translating Hindi and Chinese literature into Gujarati and vice versa. Hebrew is being considered as an addition at the moment. The DAIICT received Rs. 2 crore as part of the National Translation Mission (NTM) of the Ministry of Electronics and Information Technology to create algorithms for the Gujarati language.
IIIT-Delhi, AIIMS will work together in AI, ML, biomedical research
In order to advance clinical medicine, public health, and biomedical research, the All India Institute of Medical Sciences (AIIMS) and Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi) will collaborate in areas like artificial intelligence (AI), machine learning (ML), and computational genomics. Through the creation of cutting-edge technology, the partnership will be centred on enhancing patient care, outcomes, and healthcare delivery.
Tata Power starts trial of smart energy management system with AI capabilities
Tata Power has started a trial of a smart energy management system with AI capabilities this month. The system will be tested on 55,000 residential and 6,000 commercial and industrial Mumbai clients during the pilot. As part of this pilot initiative, Tata Power anticipates that its “Demand Response Program” would assist customers in minimizing electricity demand when energy-hungry air conditioners are turned on in the upcoming days.
UNSW academics awarded more than $1.6m for joint Australian-US AI research
Researchers from the University of New South Wales have received more than $1.6 million as part of an Australian-US initiative to create ethical and responsible artificial intelligence (AI) strategies to combat infectious diseases, environmental pollution, and drought.
Roger C. Schank, Theorist of Artificial Intelligence, Dies at 76
Roger C. Schank, a scientist who made significant contributions to the field of artificial intelligence before concentrating on human learning as an academic, author, and businessman, passed away on January 29 in Shelburne, Vermont. He was 76. The research of Dr. Schank incorporated computer science, cognitive science, and linguistics. In a 1995 essay, he stated that “trying to understand the nature of the human mind” and “creating models of the human mind on the computer” was the unifying theme of his numerous efforts in academia and business.
In an effort to challenge Google’s dominance, Microsoft has started talking to advertising agencies about how it intends to monetize its updated Bing search engine, which is powered by generative AI.
An advertising executive who requested anonymity said that during a meeting with a significant advertising agency this week, Microsoft demonstrated the new Bing and stated its intention to allow paid links in response to search results.
Microsoft expects that providing the Bing AI chatbot with more human responses will increase the number of people using its search engine and, consequently, the number of advertisers. Moreover, Bing chatbot adverts may appear on the page with more prominence than regular search results.
According to an advertising executive, Microsoft is already testing ads in an early version of the Bing chatbot, which is accessible to only a small number of people.
The business claimed to be utilising standard search ads, in which businesses place links to their websites or merchandise next to search results for relevant terms to their industry, and embedding them into Bing chatbot responses, according to an advertising executive.
Another chatbot ad type that will be customized for advertisers in particular industries is also being planned by Microsoft. As an illustration, a user might ask the new Bing powered by AI, “What are the finest hotels in Mexico? According to the advertising executive, hotel advertisements will appear.
Tata Power has started a trial of a smart energy management system with AI capabilities this month. The system will be tested on 55,000 residential and 6,000 commercial and industrial Mumbai clients during the pilot.
As part of this pilot initiative, Tata Power anticipates that its “Demand Response Program” would assist customers in minimizing electricity demand when energy-hungry air conditioners are turned on in the upcoming days.
These consumers, who are taking part in the pilot project, will receive information about the peak hour, the likely market rate, and the potential end time of the peak hour. Large office buildings, commercial complexes, and governmental organizations are also among these customers.
“The customers can defer the use of AC and switch it off during the peak hours and cool the room before the peak hour hits. By doing this, the load will come down. Also, we will be able to monitor these customers on how they optimized their use of electricity. We have proposed to provide a one-time incentive of ₹25 and ₹1 per unit saved by all those residential consumers who have participated in this pilot project,” said an official from Tata Power.
Mumbai’s power consumption is currently hovering between 2,800 and 2,900 Megawatts, and is anticipated to rise in the coming days. This method might be useful, especially with the anticipated increase in electricity use and rising temperatures.
Despite having long-term power purchase agreements, Mumbai’s power distribution companies are nonetheless forced to purchase from the open market because of the city’s growing demand.
At a recent event, Bill Gates, a co-founder of Microsoft, and UK Prime Minister Rishi Sunak answered a few questions generated by AI.
In an effort to welcome a new wave of green technology start-ups, Rishi Sunak and Bill Gates met with members of Cleantech for the UK on Wednesday at Imperial College London. They were interviewed by AI, and Downing Street posted a video of the interview on YouTube.
The two talked about innovation, technology, and artificial intelligence in the UK during their encounter. “How do you anticipate technology will effect the global economy and job market in the next 10 years,” AI’s first question to Mr. Sunak and Mr. Gates said.
Mr. Gates responded in a succinct manner. The benefactor claimed that the world needs to be more effective and gave the examples of a labour shortage, health care, and education. “Hopefully, technology like the one that generated this question can help us be more efficient,” he said.
“What is the one task you would like AI to do for you?,” the AI asked the two. Mr. Gates jokingly said that he might use the technology to make his notes a little more “smart” in response to this. Mr Sunak hilariously said, “I think if AI could do Prime Minister’s question time for me. That would be great.”
