Conversational AI can be a major asset to your social media presence. These days, 64% of people say they'd rather message a business than call it. If you're active on social media and talk to customers on your social channels, that statistic applies to you too. Every month over 1 billion messages are exchanged between people and businesses on Facebook Messenger alone. With all those inquiries and only so many people to tend to them, a chatbot or virtual assistant can be a lifesaver.

Did you miss a session from the Future of Work Summit? San Francisco headquartered Databricks, a company that offers the capabilities of a data warehouse and data lake in a single "lakehouse" architecture, today announced its first industry-specific offering: Lakehouse for Retail. Designed specifically for enterprises dealing in the retail and consumer goods vertical, Databricks says Lakehouse for Retail is a fully integrated platform that aims to solve the most critical challenges retailers and their partners face while trying to leverage surging data volumes for AI and analytics projects. The solution, which is generally available as of today, has already seen early adoption from major retail enterprises including Walgreens, Columbia, H&M Group, Reckitt, Restaurant Brands International, 84.51, Co-Op Food, Gousto, and Acosta. "With hundreds of millions of prescriptions processed by Walgreens each year, Databricks' Lakehouse for Retail allows us to unify all of this data and store it in one place for a full range of analytics and ML workloads," said Luigi Guadagno, the VP of pharmacy and healthcare platform at Walgreens.

A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us - and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge.

As Amazon's CTO since 2005, Werner Vogels has observed macro trends around the technology industry, giving him a unique perspective to distinguish substantive progress from mere fads. He recently published his views on what he sees in store in 2022 for cloud technology and the technology world in general. In his post, Werner lays out five core predictions, regarding the growth of artificial intelligence (AI), the abundance of data, the power of machine learning (ML), architecting for sustainability, and the full reach of connectivity via the Internet, backed by cloud-based resources. I think his analysis provides takeaways for startups overall and where they might seek to create value in the next year. Let's dive into each of these anticipated developments and their potential impact on the world of startups.

Imagine: social distancing restrictions are over. It's safe(r) to go out again! For once, after a long, grueling era of pandemic stress, you make plans to go out to a special public event. It hasn't happened for the longest time. Clearly, this is a cause for celebration and what else to mark the occasion than to dress yourself up a little?

This is a guest post by Jihye Park, a Data Scientist at MUSINSA. MUSINSA is one of the largest online fashion platforms in South Korea, serving 8.4M customers and selling 6,000 fashion brands. Our monthly user traffic reaches 4M, and over 90% of our demographics consist of teens and young adults who are sensitive to fashion trends. MUSINSA is a trend-setting platform leader in the country, leading with massive amounts of data. The MUSINSA Data Solution Team engages in everything related to data collected from the MUSINSA Store.

Amazon SageMaker Serverless Inference (Preview) was recently announced at re:Invent 2021 as a new model hosting feature that lets customers serve model predictions without having to explicitly provision compute instances or configure scaling policies to handle traffic variations. Serverless Inference is a new deployment capability that complements SageMaker's existing options for deployment that include: SageMaker Real-Time Inference for workloads with low latency requirements in the order of milliseconds, SageMaker Batch Transform to run predictions on batches of data, and SageMaker Asynchronous Inference for inferences with large payload sizes or requiring long processing times. Serverless Inference means that you don't need to configure and manage the underlying infrastructure hosting your models. When you host your model on a Serverless Inference endpoint, simply select the memory and max concurrent invocations. Then, SageMaker will automatically provision, scale, and terminate compute capacity based on the inference request volume.

On January 6, Mobile Industrial Robots announced the deployment of an autonomous mobile robot (AMR) at the IKEA Jarosty center, operated by FM logistic, a French logistics services specialist. With the MiR500, a heavy object and pallet transporter, this warehouse is equipped with technology that can be freely configured and used in daily operations, allowing employees to focus on more complex and higher value-added tasks. AGVs are Automatic Guided Vehicles (AGVs) that follow a path set by a wire guidance system. A wire integrated into the floor of the warehouse emits a magnetic field that is picked up by the machine and allows it to follow a defined path without any obstacles. Thanks to AI and its lasers, the ARM can navigate in an environment with operators and material handling equipment, transport, it bypasses and avoids obstacles, a great advantage over AVGs which can detect obstacles in front of them, but are not able to bypass them and stop until the obstacle is removed.

Cloud can do much more than reduce a retailer's cost of computing and data storage. In fact, it can offer new experiences to both employees and customers which are hard to imagine without being on cloud. While this may sound intuitive to digital natives, traditional organisations need to wear a different lens to understand the power of cloud. Digital solutions can enable intelligent retail that better understands customers, empowers employees to deliver breakout customer experiences, builds robust supply chains and contributes to reimagining the customer experience. A good starting point is prioritising desired business capabilities.

From January 3rd, 2022, Amazon will be solving most of its packaging issues with the help of AI based machine learning tools. Meaning, the Jeff Bezos led company will be amalgamating computer vision and natural language processing to'guestimate' the right amount of packaging required to pack millions of products it ships to its customers. According to an update released to the media, Amazon expressed that the use of AI tech has reduced the packaging consumption per shipment by over 33% that accounts for 3 million tons of packaging required to prepare over 2 billion different sized boxes. From the year 2019, Amazon tested the Machine Learning model of packaging in its facilities located across the United States and was happy to announce that it was 100% satisfied with the results. To achieve its vision, the American retail giant had to upgrade its packaging and distribution tunnels with some software driven cameras and some sensors.