The layoffs are part of a restructuring that iRobot said will save the company up to $10 million in 2022 and between $30-$40 million in 2023. This was the same day iRobot announced it was being acquired by Amazon for $1.7 billion. However, iRobot said the two events are not related. To better align costs with near-term revenue, part of the restructuring includes shifting certain non-core engineering functions to lower-cost regions and increasing use of iRobot's joint design manufacturing (JDM) partners. "These actions help support the company's near-term priorities to drive innovation by executing on its product roadmaps, optimize inventory levels across all major channels, expand DTC sales and position the business for profitable growth in 2023," iRobot said in its earnings statement.

BRIAN KENNY: Revolutions often have humble origins, a small group with big ideas gathering to plant seeds of disruption. So, it was in the dog days of summer in 1956, when 10 academics gathered on the campus of Dartmouth College to discuss how to make machines use language and form abstractions and concepts to solve the kinds of problems now reserved for humans. The conference led to the founding of a new field of study, artificial intelligence. Six decades hence, we are in the midst of an AI revolution that is already dramatically changing entire sectors like healthcare, transportation, education, banking, and retail. But AI is not without its critics. Elon Musk famously said that, "With artificial intelligence, we're summoning the demon." While Stephen Hawking believed the development of full artificial intelligence could spell the end of the human race. So, whose job is it to make sure that such a vision never comes to pass? Today on Cold Call, we've invited Professor Tsedal Neeley to discuss her case entitled, "Timnit Gebru: Silenced No More on AI Bias and The Harms of Large Language Models." Tsedal Neeley's work focuses on how leaders can scale their organizations by developing and implementing global and digital strategies.

After a period of decrease, interest in word alignments is increasing again for their usefulness in domains such as typological research, cross-lingual annotation projection, and machine translation. Generally, alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel. Here, we compute high-quality word alignments between multiple language pairs by considering all language pairs together. First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph. Next, we use graph neural networks (GNNs) to exploit the graph structure. Our GNN approach (i) utilizes information about the meaning, position, and language of the input words, (ii) incorporates information from multiple parallel sentences, (iii) adds and removes edges from the initial alignments, and (iv) yields a prediction model that can generalize beyond the training sentences. We show that community detection provides valuable information for multiparallel word alignment. Our method outperforms previous work on three word-alignment datasets and on a downstream task.

Artificial neural networks (ANNs), despite their universal function approximation capability and practical success, are subject to catastrophic forgetting. Catastrophic forgetting refers to the abrupt unlearning of a previous task when a new task is learned. It is an emergent phenomenon that hinders continual learning. Existing universal function approximation theorems for ANNs guarantee function approximation ability, but do not predict catastrophic forgetting. This paper presents a novel universal approximation theorem for multi-variable functions using only single-variable functions and exponential functions. Furthermore, we present ATLAS: a novel ANN architecture based on the new theorem. It is shown that ATLAS is a universal function approximator capable of some memory retention, and continual learning. The memory of ATLAS is imperfect, with some off-target effects during continual learning, but it is well-behaved and predictable. An efficient implementation of ATLAS is provided. Experiments are conducted to evaluate both the function approximation and memory retention capabilities of ATLAS.

Recent work demonstrates a bias in the GPT-3 model towards generating violent text completions when prompted about Muslims, compared with Christians and Hindus. Two pre-registered replication attempts, one exact and one approximate, found only the weakest bias in the more recent Instruct Series version of GPT-3, fine-tuned to eliminate biased and toxic outputs. Few violent completions were observed. Additional pre-registered experiments, however, showed that using common names associated with the religions in prompts yields a highly significant increase in violent completions, also revealing a stronger second-order bias against Muslims. Names of Muslim celebrities from non-violent domains resulted in relatively fewer violent completions, suggesting that access to individualized information can steer the model away from using stereotypes. Nonetheless, content analysis revealed religion-specific violent themes containing highly offensive ideas regardless of prompt format. Our results show the need for additional debiasing of large language models to address higher-order schemas and associations.

Ottonomy.IO has announced the close of their seed funding round of $3.3 million bringing its total funding to date to $4.9M; supporting the scale of Ottobots for fully autonomous airport, retail and restaurant deliveries. The funding round is led by Pi Ventures who back deep tech startups. Connetic Ventures and Branded Hospitality Ventures and the Founder & CEO of Addverb Technologies, Sangeet Kumar, also joined this round; making the group a dynamic mix from retail, food and robotics industry investors for Ottonomy's seed round. "Last mile delivery is the least productive, yet the most expensive part of the delivery chain. There is a strong need for automation, which Ottonomy fulfills with Ottobots," says Roopan Aulakh, Managing Director from pi Ventures.

We first analyze the consumer view to determine the momentum and awareness amongst people in social media. These days consumers look for everything on the internet and this data trail is a great source of insights. We analyze the chatter on social media, analyze search trends of the consumers & find specific pattern of internet lookups. This analysis serves as a lead indicator for the topic to understand the conversations and feedback better. We analyze 5 years of search data from 2017-08-06 to 2022-07-31.

Google experienced a major international internet outage on Tuesday, technology platforms reported. The realtime online platform Downdetector reported users had registered problems with Google explorer, the world's dominant search engine from 2.12am BST (9.12pm EST, 11.12AM AEST. As of 11.38AM, there had been 4,113 confirmed reports of Google outages. User reports indicate Google is having problems since 9:12 PM EDT. Users said sister platforms Gmail, Google maps and Google images were also experiencing problems.

In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts. Specifically, we consider two approaches: a quantum Support Vector Machine (QSVM) on a universal gate-based quantum computer, and QBoost on a quantum annealer. The quantum vision systems are benchmarked for an unbalanced dataset of images where the aim is to detect defects in manufactured car pieces. We see that the quantum algorithms outperform their classical counterparts in several ways, with QBoost allowing for larger problems to be analyzed with present-day quantum annealers. Data preprocessing, including dimensionality reduction and contrast enhancement, is also discussed, as well as hyperparameter tuning in QBoost. To the best of our knowledge, this is the first implementation of quantum computer vision systems for a problem of industrial relevance in a manufacturing production line.

Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications such as recommender systems and Electronic Health Records (EHR) mining. PARAFAC2 and its variants have been proposed to address irregular tensors where one of the tensor modes is not aligned, e.g., different users in recommender systems or patients in EHRs may have different length of records. PARAFAC2 has been successfully applied on EHRs for extracting meaningful medical concepts (phenotypes). Despite recent advancements, current models' predictability and interpretability are not satisfactory, which limits its utility for downstream analysis. In this paper, we propose MULTIPAR: a supervised irregular tensor factorization with multi-task learning. MULTIPAR is flexible to incorporate both static (e.g. in-hospital mortality prediction) and continuous or dynamic (e.g. the need for ventilation) tasks. By supervising the tensor factorization with downstream prediction tasks and leveraging information from multiple related predictive tasks, MULTIPAR can yield not only more meaningful phenotypes but also better predictive performance for downstream tasks. We conduct extensive experiments on two real-world temporal EHR datasets to demonstrate that MULTIPAR is scalable and achieves better tensor fit with more meaningful subgroups and stronger predictive performance compared to existing state-of-the-art methods.